{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 零基础实战机器学习 源代码\n",
    "\n",
    "## 第14讲 用Lifelines工具包做留存分析\n",
    "\n",
    "作者 黄佳\n",
    "\n",
    "极客时间专栏链接：https://time.geekbang.org/column/intro/438\n",
    "\n",
    "在这一讲中，我们主要分析用户留存和流失的相关因素，并进行预测。\n",
    "我们使用Lifelines工具包完成这个任务。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 安装并导入工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# pip install lifelines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>性别</th>\n",
       "      <th>玫瑰套餐</th>\n",
       "      <th>紫罗兰套餐</th>\n",
       "      <th>郁金香套餐</th>\n",
       "      <th>百合套餐</th>\n",
       "      <th>康乃馨套餐</th>\n",
       "      <th>胡姬花套餐</th>\n",
       "      <th>生日套餐</th>\n",
       "      <th>情人节套餐</th>\n",
       "      <th>会员卡类型</th>\n",
       "      <th>入会月数</th>\n",
       "      <th>会费支付方式</th>\n",
       "      <th>平均月消费</th>\n",
       "      <th>总消费</th>\n",
       "      <th>已停付会费</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>女</td>\n",
       "      <td>是</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>年卡</td>\n",
       "      <td>9</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>65.6</td>\n",
       "      <td>593.3</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>男</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>月卡</td>\n",
       "      <td>9</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>59.9</td>\n",
       "      <td>542.4</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>男</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>月卡</td>\n",
       "      <td>4</td>\n",
       "      <td>就餐时付费</td>\n",
       "      <td>73.9</td>\n",
       "      <td>280.85</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>男</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>是</td>\n",
       "      <td>月卡</td>\n",
       "      <td>13</td>\n",
       "      <td>就餐时付费</td>\n",
       "      <td>98.0</td>\n",
       "      <td>1237.85</td>\n",
       "      <td>是</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>女</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>否</td>\n",
       "      <td>是</td>\n",
       "      <td>是</td>\n",
       "      <td>是</td>\n",
       "      <td>月卡</td>\n",
       "      <td>9</td>\n",
       "      <td>花呗付款</td>\n",
       "      <td>69.4</td>\n",
       "      <td>571.45</td>\n",
       "      <td>否</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   用户码 性别 玫瑰套餐 紫罗兰套餐 郁金香套餐 百合套餐 康乃馨套餐 胡姬花套餐 生日套餐 情人节套餐 会员卡类型  入会月数 会费支付方式  \\\n",
       "0    1  女    是     是     否    是     否     是    是     否    年卡     9   手工转账   \n",
       "1    2  男    否     否     否    否     否     否    否     是    月卡     9   手工转账   \n",
       "2    3  男    否     否     否    否     是     否    否     否    月卡     4  就餐时付费   \n",
       "3    4  男    是     否     否    是     是     否    是     是    月卡    13  就餐时付费   \n",
       "4    5  女    否     是     否    否     否     是    是     是    月卡     9   花呗付款   \n",
       "\n",
       "   平均月消费      总消费 已停付会费  \n",
       "0   65.6    593.3     否  \n",
       "1   59.9    542.4     否  \n",
       "2   73.9   280.85     是  \n",
       "3   98.0  1237.85     是  \n",
       "4   69.4   571.45     否  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 导入数据包\n",
    "import numpy as np #导入NumPy\n",
    "import pandas as pd #导入Pandas\n",
    "df_member = pd.read_csv('易速鲜花会员留存.csv') #载入数据集\n",
    "df_member.head() #显示数据头几行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>入会月数</th>\n",
       "      <th>平均月消费</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "      <td>7043.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>3522.000000</td>\n",
       "      <td>32.371149</td>\n",
       "      <td>64.761692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2033.283305</td>\n",
       "      <td>24.559481</td>\n",
       "      <td>30.090047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>18.250000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1761.500000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>35.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3522.000000</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>70.350000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>5282.500000</td>\n",
       "      <td>55.000000</td>\n",
       "      <td>89.850000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7043.000000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>118.750000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               用户码         入会月数        平均月消费\n",
       "count  7043.000000  7043.000000  7043.000000\n",
       "mean   3522.000000    32.371149    64.761692\n",
       "std    2033.283305    24.559481    30.090047\n",
       "min       1.000000     0.000000    18.250000\n",
       "25%    1761.500000     9.000000    35.500000\n",
       "50%    3522.000000    29.000000    70.350000\n",
       "75%    5282.500000    55.000000    89.850000\n",
       "max    7043.000000    72.000000   118.750000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_member.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据清洗"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import is_numeric_dtype #导入is_numeric_dtype工具\n",
    "is_numeric_dtype(df_member['总消费']) #是否为数值字段？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_member['总消费'] = pd.to_numeric(df_member['总消费'], errors='coerce') #把总消费字段转换成数值字段\n",
    "df_member['总消费'].fillna(0, inplace=True) #补充0值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import is_numeric_dtype\n",
    "is_numeric_dtype(df_member['平均月消费'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#把总消费字段转换成数值字段\n",
    "df_member['总消费'] = pd.to_numeric(df_member['总消费'], errors='coerce')\n",
    "df_member['总消费'].fillna(0, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from pandas.api.types import is_numeric_dtype\n",
    "is_numeric_dtype(df_member['总消费'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt #导入绘图工具\n",
    "plt.figure(figsize=(10,8)) #图片大小\n",
    "plt.subplot(2, 2, 1) #子图1\n",
    "ax = df_member.groupby('性别').count()['用户码'].plot.pie(autopct='%1.0f%%') #饼图1\n",
    "plt.subplot(2, 2, 2) #子图2\n",
    "ax = df_member.groupby('会费支付方式').count()['用户码'].plot.pie(autopct='%1.0f%%') #饼图2\n",
    "plt.subplot(2, 2, 3) #子图3\n",
    "ax = df_member.groupby('会员卡类型').count()['用户码'].plot.pie(autopct='%1.0f%%') #饼图3\n",
    "plt.subplot(2, 2, 4) #子图4\n",
    "ax = df_member.groupby('已停付会费').count()['用户码'].plot.pie(autopct='%1.0f%%') #饼图4\n",
    "plt.show() #显示"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 特征工程"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 把汉字转换成0、1值\n",
    "df_member['已停付会费'].replace(to_replace='是', value=1, inplace=True)\n",
    "df_member['已停付会费'].replace(to_replace='否',  value=0, inplace=True)\n",
    "df_member['性别'].replace(to_replace='女', value=0, inplace=True)\n",
    "df_member['性别'].replace(to_replace='男', value=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 其它的是、否字段转换成布尔型数据\n",
    "binary_features = ['玫瑰套餐', '紫罗兰套餐', '郁金香套餐', \n",
    "                   '百合套餐', '康乃馨套餐', '胡姬花套餐', \n",
    "                   '生日套餐','情人节套餐']\n",
    "for field in binary_features:\n",
    "    df_member[field] = df_member[field] == '是'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>性别</th>\n",
       "      <th>玫瑰套餐</th>\n",
       "      <th>紫罗兰套餐</th>\n",
       "      <th>郁金香套餐</th>\n",
       "      <th>百合套餐</th>\n",
       "      <th>康乃馨套餐</th>\n",
       "      <th>胡姬花套餐</th>\n",
       "      <th>生日套餐</th>\n",
       "      <th>情人节套餐</th>\n",
       "      <th>会员卡类型</th>\n",
       "      <th>入会月数</th>\n",
       "      <th>会费支付方式</th>\n",
       "      <th>平均月消费</th>\n",
       "      <th>总消费</th>\n",
       "      <th>已停付会费</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>年卡</td>\n",
       "      <td>9</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>65.60</td>\n",
       "      <td>593.30</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>月卡</td>\n",
       "      <td>9</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>59.90</td>\n",
       "      <td>542.40</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>月卡</td>\n",
       "      <td>4</td>\n",
       "      <td>就餐时付费</td>\n",
       "      <td>73.90</td>\n",
       "      <td>280.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>月卡</td>\n",
       "      <td>13</td>\n",
       "      <td>就餐时付费</td>\n",
       "      <td>98.00</td>\n",
       "      <td>1237.85</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>月卡</td>\n",
       "      <td>9</td>\n",
       "      <td>花呗付款</td>\n",
       "      <td>69.40</td>\n",
       "      <td>571.45</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>7039</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>年卡</td>\n",
       "      <td>13</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>55.15</td>\n",
       "      <td>742.90</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>7040</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>月卡</td>\n",
       "      <td>2</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>50.30</td>\n",
       "      <td>92.75</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>7041</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>月卡</td>\n",
       "      <td>22</td>\n",
       "      <td>就餐时付费</td>\n",
       "      <td>85.10</td>\n",
       "      <td>1873.70</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>7042</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>双年卡</td>\n",
       "      <td>67</td>\n",
       "      <td>手工转账</td>\n",
       "      <td>67.85</td>\n",
       "      <td>4627.65</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>7043</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>双年卡</td>\n",
       "      <td>63</td>\n",
       "      <td>就餐时付费</td>\n",
       "      <td>59.00</td>\n",
       "      <td>3707.60</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 16 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码  性别   玫瑰套餐  紫罗兰套餐  郁金香套餐   百合套餐  康乃馨套餐  胡姬花套餐   生日套餐  情人节套餐 会员卡类型  \\\n",
       "0        1   0   True   True  False   True  False   True   True  False    年卡   \n",
       "1        2   1  False  False  False  False  False  False  False   True    月卡   \n",
       "2        3   1  False  False  False  False   True  False  False  False    月卡   \n",
       "3        4   1   True  False  False   True   True  False   True   True    月卡   \n",
       "4        5   0  False   True  False  False  False   True   True   True    月卡   \n",
       "...    ...  ..    ...    ...    ...    ...    ...    ...    ...    ...   ...   \n",
       "7038  7039   0  False  False   True  False  False   True  False  False    年卡   \n",
       "7039  7040   1  False  False  False   True  False  False  False  False    月卡   \n",
       "7040  7041   1   True  False  False  False  False  False  False   True    月卡   \n",
       "7041  7042   1   True   True   True  False   True   True  False   True   双年卡   \n",
       "7042  7043   1   True   True   True   True   True  False   True   True   双年卡   \n",
       "\n",
       "      入会月数 会费支付方式  平均月消费      总消费  已停付会费  \n",
       "0        9   手工转账  65.60   593.30      0  \n",
       "1        9   手工转账  59.90   542.40      0  \n",
       "2        4  就餐时付费  73.90   280.85      1  \n",
       "3       13  就餐时付费  98.00  1237.85      1  \n",
       "4        9   花呗付款  69.40   571.45      0  \n",
       "...    ...    ...    ...      ...    ...  \n",
       "7038    13   手工转账  55.15   742.90      0  \n",
       "7039     2   手工转账  50.30    92.75      0  \n",
       "7040    22  就餐时付费  85.10  1873.70      1  \n",
       "7041    67   手工转账  67.85  4627.65      0  \n",
       "7042    63  就餐时付费  59.00  3707.60      0  \n",
       "\n",
       "[7043 rows x 16 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_member # 显示数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 绘制生存曲线\n",
    "\n",
    "我们使用Kaplan-Meier生存曲线来查看普通客户的生存率。\n",
    "使用下面的代码，我们可以将KM生存曲线拟合到客户流失数据，并以置信区间绘制我们的生存曲线。\n",
    "生存曲线是累积的。 意思是，在下图中，在20个月后，客户不取消服务的机会刚好超过80％。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import lifelines #导入生存分析工具包\n",
    "kmf = lifelines.KaplanMeierFitter() #创建KMF模型\n",
    "kmf.fit(df_member['入会月数'], #拟合易速鲜花会员流失数据\n",
    "        event_observed=df_member['已停付会费'], \n",
    "        label='会员预期留存线')\n",
    "fig, ax = plt.subplots(figsize=(10,6)) #画布\n",
    "kmf.plot(ax=ax) #绘图\n",
    "ax.set_title('Kaplan-Meier留存曲线-易速鲜花会员们') #图题\n",
    "ax.set_xlabel('入会月数') #X轴标签\n",
    "ax.set_ylabel('留存率(%)') #Y轴标签\n",
    "plt.show() #显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 按照不同的类别比较生存曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def life_by_cat(feature, t='入会月数', event='已停付会费', df=df_member, ax=None): #定义分类留存曲线函数\n",
    "    for cat in df[feature].unique(): #遍历类别\n",
    "        idx = df[feature] == cat #当前类别\n",
    "        kmf = lifelines.KaplanMeierFitter() #创建KaplanMeier模型\n",
    "        kmf.fit(df[idx][t], event_observed=df[idx][event], label=cat) #拟合模型\n",
    "        kmf.plot(ax=ax, label=cat) #绘图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_pmt, ax_pmt = plt.subplots(figsize=(10,6)) #画布\n",
    "life_by_cat(feature='会费支付方式', ax=ax_pmt) #调用函数\n",
    "ax_pmt.set_title('会费支付方式对留存的影响') #图题\n",
    "ax_pmt.set_xlabel('入会月数') #X轴标签\n",
    "ax_pmt.set_ylabel('留存率(%)') #Y轴标签\n",
    "plt.show() #显示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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XqKLS8vX/fbip5zt8z+0bDjQjqSrQ3fXIyobPu+cO2zT0Pitm6lVVhI8bHr5hxGVrRgpmC1+8cMQANVowu/upu0d8vV4w69aldax8jZWVLzVZN1TTqjCRQDdaJa5eRa2KaVADnfpRvUpbqytmnd5jZuVL6jBV/KX93bt/Pe7FfkfT7EA3kQrdaDcx1KuoTWSqcyQPPfXMiBVMg5l6Vb1Ac+X9V477fI1WzB5c+SAAJ+52Ys3XO7kqZviSekivB7p6wW0iwaxR9QJds6+Z1A3q7bLRSFWsXjD7wM0fGO/wOobhS1JdnRboRgpujQazRj3wxLPA4yN+5ur1zzXU12fFTN2sXlWskT6y0Ty48sERQ9hIU5XQ/ilJw5eklptIuGgk0FRxM8J7r/gxDzzxLO+94sc1X5/IEh1OZaoXNTuYjbQCP3T+lKThS1JXqbeJfStvODjwJbOA5vef1Ru/wUy9qpE+sm6ekjR8Seoq9QJGvenMVt44UNWNAQYz9aNG+8jqTUnuNWsvjp13bNPGOF6GL0k9o9FgVk8joc1gJjVPI1Wx0aYk1w20buu6WgxfkvpCowGj2dU0g5lUvdGmJA1fktTBWjnNaTCTxqfZS1u0iuFLkhpUL3y0cvsog5n6VbMXfG0Vw5ckVaCVd2V2WjBzgVl1shdPfzGrnlvV1jEYviSpAt1+V+Zoi8jWU2+BWatiardT9jzFdb4kqd90QzCbyCKyTleqE9TrB9tysy1bPJpNGb4kqYN0SjBzEVl1u3r9YD/81Q9bOJLnM3xJUpfohgb/iUxXGszULwxfktQDOqXBv6o9Lw1m6iWGL0nqAd0wXTmRqlg93nmpbmP4kqQe1ykLxY5WFaun0YqZ1IkMX+ORz8Edl9d+bdoM2O3I1o5HkiaolX1k9api9Uykwb/eshfgtGS/2n/O/m39fMPXeAwMwNYj/J/Xbx9t7VgkqWLN7iOrVxWrp6qFYsFpSbWH4Ws8nn4Qrvqr2q+96BWwz+tbOx5JqlCn9JG1485LqUqGr7Ha5/XwzAgr4j55PzzX3h3SJamVWjldWc9oPWZSJzJ8jdWCN8HU6bWnHa/6K8OXJJVauezFRLkVktrB8CVJaqpOqYoBo95dWa8nzPXDVBXD13hMm1G7sf65dTBpUuvHI0ldppVVsdHurhytJ8z1w1QVw9d4jLSUxC2fhdW/aelQJKkbdcqaY2BPmNrH8CVJ6gidcnflWNgrpokwfEmSOl5VwazRfSjda1ITYfhqFle/l6S2aLTBv15P2EQWb7VXTKMxfDWLq99LUldx8Va1i+GrWVz9XpJ6RlWLt9orJjB8NYer30uSxsApSYHhqzlc/V6SOlK9dcWg8TslG23Ur8eqWP8wfEmSetZogaWRFfdt1NdEGb4kSRqHdjTqWxXrLYavZnHrIUnqe1U16lsV6y2Gr2apt/XQqidcA0ySOlAr95qsilWx7mP4agXXAJOkjtToAq2NqqJR36pY9zF8SZJUQ7OrYvUa9V24tb8YvlrBBVglqes0uypWVT9YPU5JdibDV9VcgFWSNAZOSfYPw1fVXIBVkjSKdkxJWhVrH8OXJEnj1Ox+sNGmJK2K9RbDlyRJ41SvKvTdu3/d9Y36VsWqZfhqhXoLsIJrgElSD2l1o75Vse5j+GqFeguwPvOYa4BJkhri8hXdyfAlSVKXcvmK7mT4ajfXAJMkVcQpyc5k+Gon1wCTpL7Syr0knZLsXIavdnINMEnqK63cS7Idjfoam0rCV0RMAy4HXgzcDpyWmTnsmK2ALwCzgGWZuaSKsXS1HPBOSEnqE1bF+kdVla9TgeWZuSgirgCOBK4ZdswfATdl5vsi4psRsWdm3lXReLrTZlO9E1KS+kQnVcUaZTP+2FQVvg4Dvlw+/g5wKM8PX78Bdo6IycAWwNqKxtLZ6q0BlgOtH48kqe81OiVpM/7YVBW+ZgJPl49XAHvUOOYrwLkUFbBvZua9tU4UEWcAZwDMnTu3+SNtt3prgP3yVu+ElCQ5JdljqgpfjwMzysczqP2neB7wqcz8TET8W0T8fmb+9/CDMvPTwKcBFixYkMNf71mj3Qm5fo39YJLUJ3phSlIbVRW+rgOOoph6PAy4oMYx04HBGL8G2LqisXSn0e6EBPvBJEktrYpBNWuH9ZuqwtdlwEkRcTvwI+DeiPhQZp4z5JiLgMsi4izgQYrAJkmSxqGVVTGnJJujkvCVmWuARcOePmfYMb8ADqzi8/vCk/fbDyZJaimnJJvDRVY72Uh3Qr7oFUXPVy2ujC9JUkczfHWykZrm93l90Ww/Uj+YzfiSJFrfD6axMXz1IhdnlSTR2n6w0bgA60aGL0mSVDkXYN3I8NWt6q2M/5sHbcaXJNVVxZSky1CMjeGrW43Ut7VmJdz0qdqvuTirJKnU7ClJl6EYO8NXr3FxVknSBDVSFXMZirEzfEmSpE10UqN+LzJ89RsXZ5UkdZh+uxPS8NWLGl2ctV4/2OB57QmTJDVZv90JafjqRY0uzgoj94OBPWGSJDWB4UuSJI2Zq+ZPnOFLkiSNmc34E2f40kb1mvHBhnxJkprA8NVvGmnGBxdolSRNSKOr3/finZCGr37TSDM+uECrJKlhE1n9vhfvhDR8SZKkSrn6/aYmtXsAkiRJ/cTKl8auXkP+9vuM/D77wSRJ2sDwpY1GasaH0VfHB9j3j2q/bj+YJPUF1wAbG8OXNqpXnRrL6viSpL7mGmBjY/hSczglKUnSmBi+NHG7Lhz5NackJUmjaHQNsG5l+NLE7X5M8asWpyQlSXVMZA2wbmX40tjVa8hftxq23bm145Ekdb1+XAPM8KWxq9ebNdK2Q1C/H8z9IiVJfcbwpWqN1g/mfpGSpAbV2/exnnbvCWn4UnOMuGH3vjB7z9pTku4XKUmagEbXDWv3npCGLzVHo1OSkiT1GcOXOte61U5JSlKf68VlKAxfaq96zfi7Lhx5CQunJCWpqzSy9VCvLkNh+FL1RuwHG8N+kSOFL0lSV2lk66FeXYbC8KXqjTQ96H6RkqQ+ZPhS53K/SElSDzJ8qTO5X6QkqUcZvtSZ3C9SktSjDF9qL/eLlCT1GcOX2quK/SLtB5MkdTDDl7qP/WCSpC5m+FLnmsh+kZKkrtHIAqzdzPClzlXFfpFuWSRJHaeRBVi7meFLvcctiyRJHczwpd4yln4wtyySJLWR4Uu9ZSLrgzklKUlqAcOXulOj64M5JSlJPeOBJ55taIPtl+04gxNevmMFIxobw5e6UyPN+BOZkrQqJkkd5cCXzAIeH/f7HnjiWdatH2j+gMbB8KX+MZEpyXor7VsVk6SWO3zP7Tl8z+3H/b73XvFjw5fUdK2ekrQqJkkaB8OXek+rpyStiklSZXpxAVbDlwSjT0laFZOktujFBVgNX+ovjUxJWhWTJDWR4Uv9pZEpyYk06kuSNIzhS5qoRqckJUl9yfAlDWr1lKT9YJLUlwxf0qAqpiRdUV+SNIzhS6qKVTFJUg2VhK+ImAZcDrwYuB04LTOzxnFLgJOAp4A/yMy1VYxHmrBGpiRdUV+SVENVla9TgeWZuSgirgCOBK4ZekBE7ArslZkHRMTZwE7AfRWNR5qYRqYkR2OjviT1parC12HAl8vH3wEOZVj4Ag4Hto2I7wGPAp+oaCxStWzUlySNQ1XhaybwdPl4BbBHjWNmA49l5gkRcSNwEHDD8IMi4gzgDIC5c+dWM1ppImzUlySNw6SKzvs4MKN8PKP8ebgVwN3l4/uAHWudKDM/nZkLMnPB7Nmzmz5QqePsuhC2m1f7tSfvh/uub+VoJElNVlXl6zrgKIqpx8OAC2occyvwl+Xjl2K/l3pRqxv1nZKUpI43pvAVETsDpwGD/zu+Crg6M78+wlsuA06KiNuBHwH3RsSHMvOcwQMy88aIOC0ifgjclZk3N/wtpE5VRaN+Pd4lKUkdr274iogA3glsB/xjZt5dPr8l8NqI+HdgSWY+MPR9mbkGWDTsdOcM+5nMfOsExi71J++SlKSuNlrla3fg1sy8cuiTmfkscFlEXAkcD1xS0fik3uVdkpLUl+qGr8y8OyLuHfpcRBwCvI6iWf/bmWnwkhpRxV2S9TglKUkdYSw9X3MiYiGwEvgWxTTkN4E1wHuAr1U1OKlvNVIVg8anJK2KSVLLjBq+MnN5RHwZOABYSnEH41MUdyda9ZKq0EhVbCJTklbFJHWh6VtM4bGVq8f1nnXrB5g0KSoa0diMdamJ/wIuBh4GTgDeC8wHXgB8u5KRSRqfqhZutSomqUMduscLx/2ey37wICtWr6tgNGM31vB1CfBKIIB/zcybgJsiYpvKRiapeayKSVLHGG2piT2AXTPzwhEOmRQRp2bm55s/NEk1tXrh1nqsiknqMr/zom145OnxTVU221judnxDRBwFfCozfwYQEVsAJwF/QI31uyRVqIqFWxudkrQqJqnLvPv4vbjx3ifaOoaxNNz/bUTsAvxxROwKJLAauAr4w8zMaocoqVL1piQfvbP4NdJ+ki7qKknjNqaer8z8BfA31Q5F0oQ1e0ryZ1eNHLxc1FWSGlLVxtqS2qHZU5Iu6ipJTTfWjbV3zMyHhz33isy8rZphSWq6Rhdurcd9JiVp3EYNXxGxGfCfEfHxzLwsIpYCnwH+Fji24vFJapZmV8Wq2mcSnJaU1NPG0nC/PiJWA1tFxP8BXlCuer+m+uFJ6lhVTUmC05KSetpYe74GgFuAnwJ7R8QRFHc9SuoFTklKUsuMZdrxSIqgtRPwemB7YBvgheX6X5tl5pWVjlJStTppShK8U1JSTxtL5Wun8texFHs5TqLYZHsrYDdgKmD4knpVO1bU905JST1sLD1fn42I04B/A84AHgC+C7w0My+qeHyS2q3VK+qDG31L6mlj7fmK8ve/A07PzKsi4oyKxiSpWzRSFas3JQlu9C2p542l52sysAXF1ON/A7dHxGuAGRWPTVKna6QqVm9KEtzoW1LPG8u043MR8brMXF4+dQlARPymyoFJ6mNu9C2ph4112vHIiLg0M9cDRMTSzFxa3bAkdb1Gl6+Y6J2SktThxhq+3ghcEhG3An8E/F51Q5LUExpt1B/tTslGq2JOSUrqEGMNX2vL6ceVwJnAtIjYnWL9r0cy87eVjVCSBk2kKuaUpKQOMdbwNWgVcCNwIvBmijW/FgILmjoqSb2t3pQkVLd+WL3PsyomqUXGcrfj1kN/zswvRcSbMnNJ+frbImJSZg5UNUhJPWa0MFPF+mE26kvqEHXDV0TMoVheYlpEzATuLl+aNnhMZn6kuuFJ0hjZqC+pS9QNX5n5q4iYD/wXxRITl5QvvbvqgUnSuNioL6lLjGWdr7UR8QxwAvC1iLgAuCUizqFY+X6rzDys4nFK6ieNLlMxEhv1JXWQsTbcTwU2y8xFEfEF4MbM/ESF45LUz5q9n6RVMUkdZKzh64jMXFM+PgPYo6LxSFJ9VsUkdbmx3O14LfBcRCTwaYpm+zkRcUh5yNTMfF+FY5SkjVpdFWuUVTFJIxhL5WtyZh4RES8B/hqYB/wTxSr3rwb+orrhSdI4NLsqNhFWxSSNYEzTjhHxHeAO4M+Bb1AstDoLeFlmfq+64UnSODS7KgaN94NJ0gjGEr6eAk4FFgFfodhS6D0UFbCXVDc0SWqzqtYOc0pS6mujLbI6A/gXYD2wL3Au8GHgM8BhwOZVD1CSmqKRKcmq7pJ0SlLqa6NVvrYGlgCzKcLX+ykqXw8BTwKrKx2dJDVLs6ckXVFfUoNGW+H+4YiYCnwN+AVF5SsoGu9fArw0Iv5hcJ9HSepKnVQVc0pS6nlj6fm6EvhQeexWwOlsOt04pYJxSVLrdFJVzClJqeeNZXuh97RiIJLUM6paO0xSTxjrCveS1J+qWDvMKUmprxm+JKkepyQlNZnhS5JaySlJqe8ZviSpUU5JSmqA4UuSGtUtU5JPPWAwkzqI4UuSqlDF2mGNMphJHcXwJUlV6JZNvm3iVx/6vZfMbOvnG74kqRu4nZHUMwxfktRqnTQlWY9N/FIlDF+S1GpOSUp9zfAlSd3OKUmpqxi+JKmTVDElWUVVTFLDDF+S1ElauXbYo3cWv+67fuT3uqir1HSGL0nqZfWqYj+7auTg5aKuUmUMX5LULZq9nVFV05U26kt1Gb4kqVtUcZfkSGzilypTSfiKiGnA5cCLgduB0zIzRzj2bcCxmXlEFWORJDWgHeuKSX2iqsrXqcDyzFwUEVcARwLXDD8oInYG3gg8VtE4JEmdxEZ9qbLwdRjw5fLxd4BDqRG+gI8B5wFvq2gcktQfmt0PNhr7waSGVRW+ZgJPl49XAHsMPyAiTgF+BPykojFIUv+oVzG659vNDWZVLV8h9YmqwtfjwIzy8Yzy5+EWAXOBo4E9ImJxZl44/KCIOAM4A2Du3LnVjFaSelmzg1lVy1c4Jak+UVX4ug44imLq8TDgguEHZOYpABGxC/CZWsGrPO7TwKcBFixYULNpX5LUoGbfQTmRRn2nJNUnqgpflwEnRcTtFFOL90bEhzLznIo+T5LUbK3uI6vHqph6SCXhKzPXUEwrDlUzeGXmLwCXmZCkTlPFumI26ksusipJapGqFm61KqYuY/iSJLVGVQu3WhVTlzF8SZI6Q6NTklKXMXxJksav2c347ZiSBKcl1RaGL0nS+LVy7bCqpiTBaUm1heFLktRcnXSX5Ghs1lcbGL4kSZ2tqilJsFlfbWH4kiR1ttGmJG3UV5cxfEmSWqdbGvUHx+OUpCpg+JIktU4n7SU5mnpB8KkHDGZqmOFLktS76k1JQuPTkvaKaQIMX5KkztDKKUmY+LSk1KDIzHaPYcwWLFiQt9xyS7uHIUlqtTsuh623b+45B5v1t5tX+/VGq2JPPQBTptV+zSnJvhIRt2bmguHPW/mSJPWnqpr1nZLUKAxfkqTO1+wpSai2WV+qw/AlSep8VayaP5oq1g9z+Qph+JIkdbsqqmJOSapCNtxLknqXjfpqIxvuJUlqBqtimiDDlySpd9WbkoTGpiXb0ahvr1hPMXxJknrXaKGkimb9Khr1rYr1FMOXJKl/ddNG3+oZNtxLklRLs5v1bdTvOzbcS5LUTu1o1H/qAXvFOpDhS5KkWpo9JdmORn17xTqS4UuSpFpavap+FY369dS7g7IeK2YTZviSJGm8eqFRv5GV/8GKWRMYviRJGq9mV8Xc5LuvGL4kSep0rZ6SVKUMX5IkNVMvTEnW42r7E2b4kiSpmVo9Jdnqqph3UE6Y4UuSpG5Vryr26J3Fr/uuH/m9Tle2heFLkqRWaeXaYT+7auTg5VZHbWX4kiSpVVq5dthEpivrsWI2YYYvSZL6Tb3pynqsmDWF4UuSpE7Q7CnJeupVxepxzbGmMHxJktQJWr2dkdrG8CVJUqdrZVVsIlwDbEwMX5IkdbpuqYq5BtiYGL4kSdLYNbqoq1WxDQxfkiR1s1ZOSU5kqyOrYhsYviRJ6mb1Kkb3fLt1i7p6J+SYGb4kSepV3dIr1mcmtXsAkiRJ/cTKlyRJ/aher1g9nbS0RZcyfEmS1I8avbuwiunKPrsT0vAlSZLaq8/uhLTnS5IkqYWsfEmSpOZodAHWPmP4kiRJEzeRBVj7jOFLkiSN3Uh3Sb5oX5i9Z+3+rav+yqrYEIYvSZI0do0s3GpVbBOGL0mSVC23JdqE4UuSJHWuHlwDzPAlSZI6Vw+uAeY6X5IkSS1k+JIkSWqhSqYdI2IacDnwYuB24LTMzBrHfQ7YA/g1cFJmrq9iPJIkqQd1aT9YVT1fpwLLM3NRRFwBHAlcM/SAiDgI2CwzD4iI64GjgCsrGo8kSepUja4B1qX9YFVNOx4GfLt8/B3g0BrHPAp8rOJxSJKkTrbrQthuXu3Xnrwf7ru+laNpiaoqXzOBp8vHKyimFjeRmfcARMRrgQGGVcYGRcQZwBkAc+fOrWKskiSpXfpwDbCqwtfjwIzy8Yzy5+eJiBOAs4HjR+r3ysxPA58GWLBgwfP6xiRJUocYaeshKPqz6k0TNlsH94NVFb6uo+jh+jLFFOQFww+IiDnAO4BjMvOZisYhSZJapZGth6rSwf1gVfVaXQbsGBG3A08C90bEh4Yd80ZgB+DqiPh+RJxe0VgkSVK7DVbFav166oF2j66lKql8ZeYaYNGwp88ZdswHgA9M9LPWrVvH8uXLWb169URP1bWmTZvGTjvtxJQpU9o9FEmSauukqlibdf32QsuXL2f69OnssssuRES7h9NymckTTzzB8uXLmTdvhLtFJElSx+j6JR5Wr17NzJkz+zJ4AUQEM2fO7OvKnyRJ3aTrwxfQMcFr+fLlbfncTvn+kiRpdD0RvjrBDTfcwIknnsjAwMCox5555pnceeedLRiVJEnqNF3f89UJ1qxZw1lnncWUKVM46aSTWLVqFVtssQXr1q1j1qxZfO5zn9vk+ClTpnD66aez5ZZbkpl89KMfZd99923T6CVJUisZvppg8eLFvPrVr+bCCy8EYOHChVx99dV133PJJZcwf/58BgYGqLHnuCRJgsb3fexgPRW+3vONH/OTX65o6jl/50Xb8O7j9xrx9WeeeYZ99tmHp59+muOOO46I4I477uDYY49l3bp1LFmyhK233poLLriASZOKWd7bbruN+++/n+nTpwPFHYtLlixhv/32a+rYJUnqarsuHPm1J+8vfjd89Z+tttqKs88+mze96U1cdNFF7LLLLixcuJArr7xywzFPPvkkU6dO5aGHHgLgnnvuYcWKFcyfPx8o9qx0mQhJkobp0X0feyp81atQVSUziYgNVa1att12Wy699NINPy9evJiVK1dy/vnns/vuu7dimJIkda5O2hOyBXoqfLXDNddcwwc/+EGmTJnC4sWLAbjjjjtYtKhY4H/t2rWcd955HHrooZu87/DDD+eUU05h0aJFLF26tNXDliSpc/TZ6veGrwk6+uijOfroozd57pBDDuGKK64Y8T0DAwPsv//+LFu2jJtvvrnqIUqS1Ju6tBnf8FWBVatW1X193bp1rF27lqlTp3LwwQe3aFSSJPWQLm7GN3xVYLRq1sUXX9yikUiS1KO6uBnfFe4lSZJayPAlSZLUQoYvSZKkFjJ8SZIktZAN9030l3/5l3zve99j22233fDcj3/8Yx555JHnHXvmmWfyZ3/2Z+y9996tHKIkSf2h3jIU0+fAPq9v7XiGMHw10WabbcaHP/xhFi5cuOG5V77ylTWPnTJlCqeffjpbbrklmclHP/pR9t133xaNVJKkHlZvGYoOYPhqos02e/7lrLft0CWXXML8+fMZGBggM6scmiRJ/aPeMhQw8lZGLdJb4etbfwW/uqO555yzD7zm/WM6dNKkSbz97W/fZNrx0UeLP+Abb7yRCy64YEMYu+2227j//vuZPn06UOwRuWTJEvbbb7/mjl+SJHWU3gpfbbZmzZrnTTsecMABAOyxxx5MnTqVhx56CIB77rmHFStWMH/+fADmzp3LvHnzWj5mSZI6Wg9uut1b4WuMFaqqPPXUU5x77rkbqlmwsfK13Xbbcemll254fvHixaxcuZLzzz+f3XffveVjlSSpK/Tgptu9Fb7abNmyZSxbtoyZM2dueG6w8lXL4YcfzimnnMKiRYtYunRpC0YoSZLazfDVJDfccANz5szZJHgBPPfcczWPHxgYYP/992fZsmWj7gUpSZJ6h4usNsm5557Lu971rk2eO+aYY9htt91qHr9u3TrWrl3L1KlTOfjgg1sxREmS1AGsfDXJtddey5ZbbrnJc1/72teYOnVqzeMvvvjiVgxLkqTe1aXN+IavJhkevIARg5ckSWqCLm3Gd9pRkiSphQxfkiRJLWT4kiRJaiF7viYoMxkYGGDy5MkArFq1ihUrVmz4ecqUKcyYMWPD8cuXL2ennXZqy1glSVL7WfmaoNtuu42DDjqIgw46iHnz5nH66aczZ84cZs+ezezZs3nzm9+84dgbbriBE088kYGBgTGd+8wzz+TOO++sauiSJKkNrHxN0H777ce73/1ujjnmGN7xjncwb948vvjFL3LIIYewcuVK9txzT6DY9/Gss85iypQpnHTSSaxatYotttiCdevWMWvWLD73uc8979xTpkzh9NNPZ8sttyQz+ehHP8q+++7b6q8oSZKayPDVBJdffjkPP/ww99xzD+9///v5yle+wjXXXMMXvvCFDVOOixcv5tWvfjUXXnghAAsXLuTqq68e9dyXXHIJ8+fPZ2BggMys9HtIkqTq9VT4+sDNH+CnT/60qeecv918zn3luXWP+eQnP8mhhx7K7NmzmTx5MjvvvDN33XUX1113HR/5yEd45pln2GeffXj66ac57rjjiAjuuOMOjj32WNatW8eSJUs48sgjufHGG7nggguYNKmYDb7tttu4//77N2zUnZksWbKE/fbbr6nfUZKknlNvAdZpM2o/3yI9Fb7aZfPNN+f444/n8ssvZ/369Rx33HG85z3vYfLkybzgBS8A4Oyzz+ZNb3oTF110EbvssgsLFy7kyiuv3OQ8e+yxB1OnTuWhhx4C4J577mHFihXMnz8fgLlz5zJv3ryWfjdJkrpSvQVY26ynwtdoFaqqPPPMM3z1q1/l5JNP5oILLuCtb30rf/iHf8i3vvUtoKhYRcSGilYtmcl2223HpZdeuuG5xYsXs3LlSs4//3x23333yr+HJEmqnnc7TlBm8pa3vIVzzz2XP//zP2fevHm8+c1v5p3vfCdve9vbuPfee7nmmms44ogj+OUvf8nixYtZtGgRd9xxB4sWLWLRokUcddRRXH/99TXPf/jhh3PKKaewdOnSln4vSZJUjZ6qfLXDT37yE3bbbTde+9rX8v3vf58PfvCDLF26lNe85jUceuihHHHEEfzHf/wH11577SbvO+SQQ7jiiivqnntgYID999+fZcuWcfPNN1f5NSRJUosYviZor732Yq+99gJgwYIFfP3rX2f77bcH4OCDD+bOO+9kq622et77Vq1aNeq5161bx9q1a5k6dSoHH3xwcwcuSZLawvDVRNOmTWPatGmbPFcreAFjqmRdfPHFTRmXJEnqHD3R89Xv61/1+/eXJKmbdH34mjZtGk888UTfBpDM5IknnnhexU2SJHWmrp923GmnnVi+fDmPPfZYu4fSNtOmTXOzbkmSukTXh68pU6a48KgkSeoaXT/tKEmS1E0MX5IkSS1k+JIkSWqh6Ka7BCPiMeCBij9mFvB4xZ/Rrbw2tXldavO6jMxrU5vXZWRem9o6/brsnJmzhz/ZVeGrFSLilsxc0O5xdCKvTW1el9q8LiPz2tTmdRmZ16a2br0uTjtKkiS1kOFLkiSphQxfz/fpdg+gg3ltavO61OZ1GZnXpjavy8i8NrV15XWx50uSJKmFrHxJkiS1kOFLkiSphQxfpYiYFhFXRMSPIuLSiIh2j6ndImJKRHyjfOz1KUXE5yLipoj4ekRs7XUpRMRmEfEfEbEsIv7Ff2Y2FRFvi4hrvS4bRcT+EbE8Ir5f/vpdr00hIpaU/535VkRs43UpRMTCIf+8PBQRb+zGa2P42uhUYHlm/i6wLXBkm8fTVhGxBXArG6+D1weIiIOAzTLzAGAb4HS8LoNOBH6UmQcCOwCL8doAEBE7A28sf/TfpY22BT6VmQdl5kHA/nhtiIhdgb3K/858CzgZrwsAmXn9kH9ebgdm0IXXxvC10WHAt8vH3wEObeNY2i4zV2Xmy4Dl5VNen8KjwMfKx5OApXhdBl0FfCQiNgNeALwCr82gjwHnlY/9d2mjbYHXRcTNEfFl4HC8NlBch20j4nvAwRTXwesyRERsCbwUOIAuvDaGr41mAk+Xj1cA27VxLJ3I6wNk5j2ZeXNEvBYYAP4HrwsAmfnbzHwWWEYRUv1nBoiIU4AfAT8pn/K6bPRz4F2Z+UqKaulJeG0AZgOPZeargZ2AF+J1Ge5I4Dq69N8nw9dGj1OULyl/7+S9otrB61OKiBOAs4HjgV/hdQEgImZGxFTg9ykqGnvjtQFYRFHJ+CKwH7AAr8ugXwDXDnk8gNcGihBxd/n4PmAhXpfhjgeuoEv/bjJ8bXQdcFT5+DDgu20cSyfy+gARMQd4B7AoM1fidRnq7cAbMvM54Fng7/DakJmnlP0pJ1P0Ub4Dr8ugtwEnR8QkirD+drw2UPxzMrhf4Usppqy9LqWyqX4hxTRjV/432PC10WXAjhFxO/AkxR+oNvL6FN5IMT1ydUR8H5iC12XQRcDpEXEj8ATwz3htavHfpY0uBN4E/AD4Cv4zA0Bm3gg8ERE/pKiAfQyvy1D7Az/JzNV06b9PrnAvSZLUQla+JEmSWsjwJUmS1EKGL0mSpBYyfEnqCxGxQ7dsPSKptxm+JHWtiJgUERdHxEtHOe6FwA+Bc+scc0FE3Fruvzj465Fhx5wXEQdHxN9HxLkRsW1EXBIROzbnG0nqB4YvSV0rMweAfwK+Uu6H9zwRMYPidvQ/APYsNyyuVQFbD7w9M48Y/AU8NOQ8AdwGTC+PfZZiMcx9gB3KTaLnNPHrSepRhi9JXS0zbwE+DHxy+GtlRewa4G8y81aKNaVeANwWEadHxLZDDl9f4/QDQx7vCpxDsVL9yRQbiZ8H/AY4FrgEmDuxbyOpH2zW7gFIUhO8Dtg6IvbPzB8CRMS+wGcoFvL8h4j4bXnsdODvKYLYTcBT5fMDwIcj4qkh591+yOMHKBZwfBT4NbAH8CLgcuBLwK6ZeXMF301SjzF8SepqEXEwRdXqr4ElwBsAMvN/IuLVwKrM/Oyw90wCvpKZQ6tdUymmHa8fctxNQ16fCdwJbE6xp9wAcDTFtjg3Ag8395tJ6lWGL0ldKyK2ophufG1m/jwi3hsRh2fm4BYjHwdeGhHrgN2Bn5XPTwLuAs4acrptgQ9ExMohzw2tfL0O+GOKqcWFwLOZuSgiPkMxBfk/Tf1yknqW2wtJ6koRsQXwDeCywcpWROwNXAWckpnfK5/bHHg/RW/Wh4ClwLszc9Ww8/0UODAznxjy3E2ZecCQn88GXk4RtFZk5uci4lDg34F5mflMNd9WUi+x4V5S14mI3Sg2Y75i6JRiZt4JnEpx9+NREfEq4McU1arZwBeAk4BvR8Q2Q853MPCrocGrNHnYz5+g2Oj4/wJ/GhFzKapny4FDm/cNJfUypx0ldaOfA2+u1eCemddHxH7AgxQN8RcC91H0ZP0KeAI4rnxtRfm2DwDvGnqeiLgKuGfIzwcA5wPfB14JvIyi8vbXFD1f34yIxzLzB837mpJ6kdOOkvpeRGyZmc8Oe25qZq4Z8vPmQGbmulrvi4hJ5bpjklSX4UuSJKmF7PmSJElqIcOXJElSCxm+JEmSWsjwJUmS1EKGL0mSpBb6/wGt0ysrLYxfZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_contract, ax_contract = plt.subplots(figsize=(10,6)) #画布\n",
    "life_by_cat(feature='会员卡类型', ax=ax_contract) #调用函数\n",
    "ax_contract.set_title('会员卡类型对留存的影响') #图题\n",
    "ax_contract.set_xlabel('入会月数') #X轴标签\n",
    "ax_contract.set_ylabel('留存率(%)') #Y轴标签\n",
    "plt.show() #显示图片"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig_gender, ax_gender = plt.subplots(figsize=(10,6)) #画布\n",
    "life_by_cat(feature='性别', ax=ax_gender) #调用函数\n",
    "ax_gender.set_title('性别对留存的影响') #图题\n",
    "ax_gender.set_xlabel('入会月数') #X轴标签\n",
    "ax_gender.set_ylabel('留存率(%)') #Y轴标签\n",
    "plt.show() #显示图片"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cox比例危害模型\n",
    "\n",
    "为了检查不同功能的影响，我们将使用Cox比例危害模型。 \n",
    "我们可以将其视为生存回归模型。可以认为“危险”会增加/减少生存机会。 \n",
    "例如，在我们的业务问题中，危害可能是客户拥有的合同类型。具有多年合同的客户取消合同的频率可能比具有月度合同的客户取消合同的频率低。\n",
    "一个限制是模型假设各组之间的危害随时间变化的比率是恒定的。 Lifeline为CoxPHFitter对象提供了一个内置的check_assumptions方法。\n",
    "经过一些数据清理，包括编码分类变量（k-1虚拟变量），我们可以将生存回归模型拟合到数据中。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 数据整理\n",
    "先做数据整理工作，把每个数据字段都转换为可以处理的字段"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>用户码</th>\n",
       "      <th>性别</th>\n",
       "      <th>玫瑰套餐</th>\n",
       "      <th>紫罗兰套餐</th>\n",
       "      <th>郁金香套餐</th>\n",
       "      <th>百合套餐</th>\n",
       "      <th>康乃馨套餐</th>\n",
       "      <th>胡姬花套餐</th>\n",
       "      <th>生日套餐</th>\n",
       "      <th>情人节套餐</th>\n",
       "      <th>入会月数</th>\n",
       "      <th>平均月消费</th>\n",
       "      <th>总消费</th>\n",
       "      <th>已停付会费</th>\n",
       "      <th>会员卡类型_年卡</th>\n",
       "      <th>会员卡类型_月卡</th>\n",
       "      <th>会费支付方式_微信自动扣款</th>\n",
       "      <th>会费支付方式_手工转账</th>\n",
       "      <th>会费支付方式_花呗付款</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>9</td>\n",
       "      <td>65.60</td>\n",
       "      <td>593.30</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>59.90</td>\n",
       "      <td>542.40</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>4</td>\n",
       "      <td>73.90</td>\n",
       "      <td>280.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>13</td>\n",
       "      <td>98.00</td>\n",
       "      <td>1237.85</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>9</td>\n",
       "      <td>69.40</td>\n",
       "      <td>571.45</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7038</th>\n",
       "      <td>7039</td>\n",
       "      <td>0</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>13</td>\n",
       "      <td>55.15</td>\n",
       "      <td>742.90</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7039</th>\n",
       "      <td>7040</td>\n",
       "      <td>1</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>2</td>\n",
       "      <td>50.30</td>\n",
       "      <td>92.75</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7040</th>\n",
       "      <td>7041</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>22</td>\n",
       "      <td>85.10</td>\n",
       "      <td>1873.70</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7041</th>\n",
       "      <td>7042</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>67</td>\n",
       "      <td>67.85</td>\n",
       "      <td>4627.65</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7042</th>\n",
       "      <td>7043</td>\n",
       "      <td>1</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>63</td>\n",
       "      <td>59.00</td>\n",
       "      <td>3707.60</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7043 rows × 19 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       用户码  性别   玫瑰套餐  紫罗兰套餐  郁金香套餐   百合套餐  康乃馨套餐  胡姬花套餐   生日套餐  情人节套餐  入会月数  \\\n",
       "0        1   0   True   True  False   True  False   True   True  False     9   \n",
       "1        2   1  False  False  False  False  False  False  False   True     9   \n",
       "2        3   1  False  False  False  False   True  False  False  False     4   \n",
       "3        4   1   True  False  False   True   True  False   True   True    13   \n",
       "4        5   0  False   True  False  False  False   True   True   True     9   \n",
       "...    ...  ..    ...    ...    ...    ...    ...    ...    ...    ...   ...   \n",
       "7038  7039   0  False  False   True  False  False   True  False  False    13   \n",
       "7039  7040   1  False  False  False   True  False  False  False  False     2   \n",
       "7040  7041   1   True  False  False  False  False  False  False   True    22   \n",
       "7041  7042   1   True   True   True  False   True   True  False   True    67   \n",
       "7042  7043   1   True   True   True   True   True  False   True   True    63   \n",
       "\n",
       "      平均月消费      总消费  已停付会费  会员卡类型_年卡  会员卡类型_月卡  会费支付方式_微信自动扣款  会费支付方式_手工转账  \\\n",
       "0     65.60   593.30      0         1         0              0            1   \n",
       "1     59.90   542.40      0         0         1              0            1   \n",
       "2     73.90   280.85      1         0         1              0            0   \n",
       "3     98.00  1237.85      1         0         1              0            0   \n",
       "4     69.40   571.45      0         0         1              0            0   \n",
       "...     ...      ...    ...       ...       ...            ...          ...   \n",
       "7038  55.15   742.90      0         1         0              0            1   \n",
       "7039  50.30    92.75      0         0         1              0            1   \n",
       "7040  85.10  1873.70      1         0         1              0            0   \n",
       "7041  67.85  4627.65      0         0         0              0            1   \n",
       "7042  59.00  3707.60      0         0         0              0            0   \n",
       "\n",
       "      会费支付方式_花呗付款  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               1  \n",
       "...           ...  \n",
       "7038            0  \n",
       "7039            0  \n",
       "7040            0  \n",
       "7041            0  \n",
       "7042            0  \n",
       "\n",
       "[7043 rows x 19 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#把分类字段转换为哑编码\n",
    "category_features = ['会员卡类型', '会费支付方式'] #要转换的分类的字段\n",
    "df_member = pd.get_dummies(df_member, #哑变量的个数会比总类别数少1 \n",
    "                           drop_first=True, #这是因为drop_first=True\n",
    "                           columns=category_features) #这能够避免回归中的多重共线性问题 \n",
    "df_member #显示数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 创建并拟合模型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<lifelines.CoxPHFitter: fitted with 7043 total observations, 5174 right-censored observations>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cph = lifelines.CoxPHFitter() #创建模型\n",
    "cph.fit(df_member, duration_col='入会月数', #拟合模型\n",
    "        event_col='已停付会费', show_progress=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 绘制会员的生存曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 生存曲线绘制\n",
    "cph.predict_survival_function(df_member.loc[3]).plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分析影响留存的因子"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4PXAeKRjMiohnc7PCUY59WBdwoOcbHPwOWCjpyoiYnc+3DNgXQFIlcD1phOPzEfGikR1JI/I6m+eAbYEKSccAo4Ev9uHy9gP2i4j/FvR5Sw/tu6zZysu0adNKXYKZmVnZq6ysZNasWVRWVpa6lC5VVFRQVVXV5UhGVVUVFRUVJaiqZ+X6napMHnOyWUh6I/Az4E7SovmXAXsCjwJvj4iOvP7klcC/gctIi+q/AvwCWBQRT3fR7zzSmpb7gb0joilvF/Bq4ADStLHTSNO0fgEsBa4F/gA8Q/pdrM7HTQZeGxHn588jgBGFz4EpOn8j8MV8V7TC7bdFxBu6OabLmnvS0NAQS5cu7a2ZDaCZM2cyd+7cUpdhZmZmG8nPeekfScsioqF4+7AaeQEeIS3Yvz+/fxT4L/AOYMc85epTwF6k7+YjEfF3SacAs4AjJb2ri4dHjgbGRMRK0noZACIi8h3NWoApEfEEgKS3kBbjvxtYDLwR+IKkF4UTSQfmtyNI085+0c11fQX436JjrwX+0cN30WXNVl6am9dbhmRmZmaDUHV1tUPKABhWIy99IWk/4IGIeKSLfTt1tX1zkXRT0aaVEfFWSVtExDNFbcfmYDJgPPKy+U2dOtW3SjYzM7NhxyMvfRQRf+hhX8mCSz7//t1sf6aLbQMaXKw0ym2eqZmZmVkp9bQA3cxKbN68eaUuwczMzKxsOLyYlbFFixaVugQzMzOzsuHwYlbGHF7MzMzM1nF4MTMzMzOzQcHhxczMzMzMBgWHF7My5gdUmpmZma3j8GJmZmZmZoOCw4tZGZs5c2apSzAzMzMrGw4vZmZmZmY2KAzb8CJpB0kqdR2dJI0q+jxyA/qoGbiKzMzMzMzKy7AML5K2B24DPtPN/j9JWlL0WtFDf9+VtGcP+z8nqVLSdEnHSxoj6QJJ4/L+ccCVkh6TdKWkfwIfkfSqvP+aPlxTI3C5pD79Tnur2crDEUccUeoSzMw2u9bWVh544IH1Xq2traUuzcxKbNiFF0nbABcD7wZ2l3RGFyMwT0bE5MIX0NJDt6uAH+SQc6OkSflckjQGaAIOBp7Pbd8M/BFYK0kR8VxEHAL8KSLeCfw+Ir4HfCn339HLNY0FLgIE/D9J10m6XNJVkn7cn5qtvDi8mNmm0t7ezuzZs2lvby91KS/S2tpKfX09dXV1673q6+vLLsCU6/doNlSN6r3J0CGpnhRcZkTEMknHkwLC7ZK+BfwyItqArSRdX3T4Tr10/+GIuDePfHSGoTrgh8CzwA7Aq4C/AP/K+08AjpI0EXgL8HJJpwK7SDoWWJnbRS/nvpAUeD6er3NJRLytl2O6q9nKyEknncS8efNKXYaZDUHt7e3MmTOH4447jsrKylKX84KOjg7a2tpoamqipmbdbOiWlhYaGxvp6Oigurq6hBW+WLl+j2ZD1bAJL3lkYQHpD/2vSnoq79oaOBs4HrgFaIuIN/XS1z7Ap4C1edPrgDpJT65roq9GxDLgLZLqgG8BvySN4LwV+L+IuDw33gJ4GngIuIM0KvR34D19uK4tgbuAbSRdRQo6r5Z0NTAa+GpE/LafNVuZ8L/kmdmm1tLS08SCza+znpqaGmpra7vdXy7KrR6zoW7YhJeIWC7pzcCzEfHDwn155OGXEbFa0itJ07zuLupiN+BdEXE78DfSqMjL8r5XAhXAvfnzQ0Bzno72BaAK+DBwKCksfQg4RdJpEfE14DHgbcC5EbFE0hkR8UdJsyS9iR5GRSLiaeCbkn4InBwRD+SRl7cXNe1TzcX9S5oOTAeYOHFid2WYmdkg1djYWOoS+mWw1WtmA2vYhJfsm0C9pFXALqTRDUhrf+4BTiatBVkSEUflsDM2j1x8hzxqERGtpAACgKQLSaFkbkT8vWD760mh5D/AD4CXA+OAQ3KTcZLuBf4JXBIRN+TtsyUdFRGXSvoAMLu7C8prZoJ1IyrdtelTzcUiYj4wH6ChoaG36Ws2wOrq6kpdgpkNccXTs0qtc3pYdwZbvWY2sIZVeImIj+YF9F8Gfg+cRwoGsyLi2dyscJRjH9YFHOj5Bge/AxZKujIiZufzLQP2BZBUCVxPGuH4fES8aGRH0oi8zuY5YFugQtIxpKlfX+zhvAdLmkEKXRfmew+8WtKVef8Y4Bzghi6OXa9mKy9z584tdQlmNsR1Nz2r1IqnY/U2nczMhodhFV4kvRH4GXAnKRQsBPYE9pX09ojoIE3hOkfSjsBhwAmSrgB+QZp61ZURpFsv7wfsXXA+Aa8GDiBNGzuNNE3rF5KWAtcCfwCeAe6MiAPzcZOB10bE+fnzCEmjImJ18Ykj4jrguqLrvDHftawnXdZs5WXBggVMmzat1GWY2RBUWVnJrFmzym6ReUVFBVVVVV2OZlRVVVFRUVGCqrpXrt+j2VClNONoeMgPcTwCuB94BHgU+C/wDuCvpHByJrAXKdh9NiL+nhfczwJeQlr3sqao3+8BF0bEnV2c8xukRfoLIuKJvG0kaTH+W4FTgAbS2pj1wkk2AvhBRPyij9d5a0T0GEh6qrk7DQ0NsXTp0r42twEwdepUFi5cWOoyzMw2q9bWVjo61n9KQEVFRVndaczMNh1JyyKiYb3twym89IWk/YAHIuKRLvbt1NX2zUXSTUWbVkbEWzfX+R1eNj+HFzMzMxuOugsvw2raWF9ExB962Fey4JLPv38pz29mZmZmVko9LUA3sxK76KKLSl2CmZmZWdlweDErY83N6z16x8zMzGzYcngxK2PnnXdeqUswMzMzKxsOL2ZmZmZmNig4vJiZmZmZ2aDg8GJWxvyASjMzM7N1HF7MytiUKVNKXYKZmZlZ2XB4MStjU6dOLXUJZmZmZmXD4cXMzMzMzAYFhxczMzMzMxsURpW6AEk7AI9GRHSzf3RErBqgc+0E/CciVg5EfxtRxwhgDFABVAITgDqgAZgbEY9tQJ8q/A4ljYuI5wamYiuVSZMmdbm9tbWVjo6O9bZXVFRQXV29qcsyMzMzK4mSjrxI2h64DfhMF/tG57cnSvqwpE9KOqiX/j4raXoPTb4MfKDomNskbdFNf8dI2r/Hi0jtJOkeSbWSju6tPfAL4E/A/wGXAl8CdgJuB8YV9DtSkro43whJIws+bwP8vqjZrZJ6DKeSfilpjKTtJL22D3XbAGtvb2f27Nm0t7d3uX/GjBnrbWttbaW+vp66urr1XvX19bS2tg7Iuc3MzMzKTclGXvIf3BcD7wY+KekM4NyC0YOrJY0hjUqsIf1R/6SkZ4BHIuL9knYGfgQ8AwRp9GKVpHflPrYEPhoR90vaEngT8JGiUp6OiGdyTaMAImJ13vcMsAdwU94/AlBErCnsICJC0nNACzBJ0lURsf4/i69r/96C7+E4YLuI6OpR6icBh0sKUrhZC/wLGAn8HJgn6XjgY0CVpOuA7wLTgJcBl+drOgLYBlgO/AN4OXAAsDIinpf08dznHd3VbJtGe3s7c+bM4bjjjqOysnK9/eeee+56Aaajo4O2tjaampqoqal5YXtLSwuNjY10dHT0afSlt3ObmZmZlZuShBdJ9aTgMiMiluU/wL8E3C7pW8AvgXNIf2DvCjwFbAs8nF9bAUTEg7lNZ7+nAu0R8aMuTjsdeIQUBk4GOgPKXpKuII2CPA2cLGkNKSyMBe6X1Bk2BFyYg9B04PmC/ncGrsxtrpX01oh4tpvrfyPwrXz89sBoSYeTppJ9IiL+lK/vW7kdkk4Eniu+toj4Ye7vYuDP+bu6mRQEP9Q57S6PLl0D3AvUAKtyvxOBtwGTu6rVNo+WlpYut9900028//3v77JtTU0NtbW1fe5rQ9uZmZmZlYvNHl4kTQIWABcCX5X0VN61NXA2cDxwC/Aa4HBSUOnIP3cGnsj97BwRD+YgNIs0KrE78LykycDtEfHN3HZH4IPAExHxc9KoRWc9SyKic6QG4PK8/X+AcRFxQTeXcmlBHyJNBTupL+tVcjjZOx97HN2PvPRKUgWwHdBI+g7fQBrN2kbS9cAIST8Ars2H7A88QApZAC8hBbHVkkZExNqi/qfn/UycOHFDSrQ+aGxs7HbfFVdcMWB9mZmZmQ1mmz28RMRySW8Gno2IHxbuy9OyfgnUk6aA/Rt4KfAgKcxAWtuxH9CWP48mjRy8BLgqb3s9aU1Jp0nAGcCZ+TzHAlURcX4Ppe4IvFvSYQV9bNc5rU3S1cBE0nSrP+fX64Gre7p+SVOAc8kjH7nuUZLelz+PAmZHxJU99VPgnaSpbbsAh5KmjD1Gmlr364LzviS/7VzndCpp1Opl+VofBOYANxR2HhHzgfkADQ0NXd5UwTZe8RSwTqeccgoXXPDi/Nw5Pay/fRXrrR8zMzOzclOqNS/fBOolrSL90f33vH0EcA9wFukP8h2ARXnfvfnnXsCrO9eURMQ9wPaSbgROAB4ljTL8rPNkEXEVgKQz86bfkqZQnd9DjQcBR+f+kbS08G5eEfH2PGoyLiK+I2k/4MP0El4iYjEp5JD7PY6NGHkBLiONAp0B3EUaVRkJnCJpFrBzRLyEdSMtnb/zC0jB6XjguxHx4Q08vw2A7qaA/epXv+r2mOJpX71NJzMzMzMb7EoSXiLio3kx/pdJIynnAbOBWQXrRA6SdEVEnJjXe6yKiO9LuqYzUBSZC8wjLUi/IiKe7OH8/5S0d3f78523xhcEl61I62G6a78DKYC9WtIE0vfa1nkjgE3s/cCxpLUze5LCy0rSNLmfAh/N7daSgt02wDLgWYCIeFbSFpJqIsKLIDazyspKZs2a1e2C+cWLFzNlypQXbauoqKCqqqrLUZOqqioqKioG5NxmZmZm5aZUC/bfSBoZuZM07Wsh6Q/vfSW9veBOXb+W1Ah8CDgtr91Y2k237cDrSAvPP97Nc05euO1wwbNexhTVtjNpJOOEgs27Ao8XtJkAHAO8gzT9a2fgIuArwHdIAeIe8nSr7kjalrR4fr2Qkxfwn0YKIpCmz62VdEz+PJ4U2P5fRFwi6XOku4ndCxxIClOfiIg78/X+R9KDud698kL/UXltz1eASyV9KCKae6rZBlZlZSWzZ8/udv+CBQvWCy/V1dWsWLFio5/z0tu5zczMzMpNqaaNPUJasH9/fv8o8F9SGNgR6JA0D9iXdPveD0bEA/l2xKdJOi8iTpf0ctIi/1eTwsLhpLttfRb4mqSVwDER0ZTX0xTeHQxJ1wIrCj6PJIWPGRHRlLd9C3gXcHLBoU+RgtAHI+JfBdtbJNUA7wW+0Ifv4SOkUZMjindExOXkmwf00WhgVL6N8wWSfg/8MI9etUvaJ5/nEOAzkl5FuqPatqSRmM9T8IwZK2/V1dV+GKWZmZkNO+rmwfYl13mL3z60Oxj4Y/E0sXwHsFcCzX3pp4f+RxU896W/x95UtGllRLx1Q2vZgPO/MPqUR3mIiP8W7B8TEc93d3yxhoaGWLq0u4Ev2xSmTp3KwoULS12GmZmZ2WYlaVlENBRvL9lDKnvT18AREb/pZnuw7kYAG1PHBgWXfOz+G3v+jVE4ba4wtBRs63NwsdI4/fTTS12CmZmZWdkY0XsTMyuVurq6UpdgZmZmVjYcXszK2Mknn9x7IzMzM7NhwuHFzMzMzMwGBYcXMzMzMzMbFBxezMpY8TNezMzMzIYzhxezMjZt2rRSl2BmZmZWNhxezMrYzJkzS12CmZmZWdlweDErY83NzaUuwczMzKxsOLyYmZmZmdmg4PBiVsYqKytLXYKZmZlZ2RhV6gKGGkkjgbUREUXbRwCKiDVdHDM2IlZuxhpHR8SqzXU+23Dz5s170efW1lY6OjrWa1dRUUF1dfXmKsvMzMysJBxeBt4ngHdJWgtsD4wDHiKNcv0U+KGkVwBnRcQH8zG/lnRmRCwt7kzSN4A3A20Fm18VETsUtJkB/AioB54Cnga+FBFTJU0DLo6IZwtCy4mSngAqgXsi4rcDeP02gBYtWsQRRxwBpOBSX19PW1vbeu2qqqpYsWKFA4yZmZkNaQ4vAywizpf0s4j4j6T3AbURcZ6kXYB/5GYrgVUAkrYCXg+8U9I78/6zImJtfr8aOC0ilnSeQ9KtRaddCnwHmAl8HzgaeF7Su4FJEbEgt7ta0hhgArCGFKyelPQM8EhEvH+AvgYbIIXhpaOjg7a2NpqamqipqXmhTUtLC42NjXR0dDi8mJmZ2ZDm8DLAJAn4paSDCraNBn4F7CdpL+AcoFbSJcAt+fO1ufkPI2J2QZeruzhNZ7DpDD8PA+8jjaRcSBqp2YkUTn5RcNw5wAHArqQRmm3zsQ8DW23QBdtmV1NTQ21tbanLMDMzM9vsHF4GWESEpMuAd7EuZEwHfhQRrZJuBD4AnAWcCfwSOB8YGRF35lGQQmuBr0kqnCs0oeD9S4ELgJ8B95NCzE+Bi/L+OcAN+f1rgMNJQaUj/9wZeAJA0s4R8WDhySVNz/UzceLE/nwVZmZmZmYDyuFlAOXF+gDfjojVedoYwI+BNZJGAUEONRHxT0mHkkZMPg18HFBRt2NZf9rYLZ3vI2IF8A5J44FXkaagbVNw/Np8zG5AHfBvUuB5kBRmAH4P7MeL19V09j8fmA/Q0NAQxftt05o7d26pSzAzMzMrGw4vA2sKcAYpqEAaIRkPHJj3jwa+DtwJ6Q5keTRmFXCnpK7CSxXwFUlPFmwrHHlB0nbA/5JGXC6NiM8W7FuS37YBewA7AIvytnvzz72AV0fE+rexsrLT0tLS42czMzOzocrhZQDlu3a9cOeuwgX7BdsmAIcBBwPfl3QvaarXhaTwcWRRt/sB+0XEfwv6uKWozWGsCyJHSWroorbHgIMkXRERJ0o6EVgVEd+XdE1E3LNhV22b0syZM1m4cCGQbodcVVVFY2Pjeu2qqqqoqKjY3OWZmZmZbVYOL5tW8SgKpDUntcDkiPi7pGrgq3mtzNaku4Clg6VG4NHC4JKNLPo8jbSOZkcKRl7ys2UWF7X9de73Q8Bpkn5AuluZlbnq6mpWrFjh57yYmZnZsOXwsmmNJU0Ve0FEfLfocyuApBOA04CvFez+Cmk62AskXcu6Wy4j6aXAfyPiEUl1Ref/NnBbQdt5wL7AHcAHI+IBSc+RQsx5EXH6hlykbT7V1dUOKWZmZjZsqehB8FZGJG0REc8UbRsbESv7ePzIiCgcyel8SOUGaWhoiKVLPUizORU+58XMzMxsuJC0LCLWWwoxohTFWN8UB5e8rU/BJbddU/R5g4OLlYaDi5mZmdk6Di9mZeykk04qdQlmZmZmZcPhxayMtbe3l7oEMzMzs7Lh8GJmZmZmZoOCw4tZGaurK76BnJmZmdnw5fBiVsbmzp1b6hLMzMzMyobDi1kZW7BgQalLMDMzMysbDi9mZWzx4sWlLsHMzMysbDi8mJmZmZnZoODwYmZmZmZmg4LDi1kZu+iii0pdgpmZmVnZGFXqAmxgSdoZ2D4ibit1LbZhWltb6ejoAODuu+9mzz33BKCiooLq6upSlmZmZmZWUg4vZU7Sh4AzgUeA7YFxwENAFfD7iPhU0SGjgGOB24r6GQkQEWskjQDGA/sBz+QmT0XEHZvoMqyPWltbqa+vp62tbb19VVVVrFixwgHGzMzMhi2Hl/L3PHBBRHxH0vuA2og4T9Jk4NDORpL+D5hQ8HlJfvuziFgAHAicLun1pGBzKfBSYBUgYEphf1YaHR0dtLW10dTURE1NzQvbW1paaGxspKOjw+HFzMzMhi2Hl/K3FjglB5ftgXGSDiGNvPy2oN0OEfHmwgNzwDkAICKuk/Rf4LiI+HjefyTwOPAXoHbTXob1R01NDbW1taUuw8zMzKyseMH+4HBBRBwInAXMz+9PK2qjbo4NAElbAN8Gtpb0I0njgUnAv4GXkaalrUfSdElLJS3997//PQCXYmZmZma2YTzyUv76+juqL5gq1qkS+FV+/2lSwPkGcDop1NQD3wK2AbaQ1BwRlxR2EBHzgfkADQ0NsQH1m5mZmZkNCIeX8rcV8Mmepo1J2hL4V0RMLjxQ0uuAvfPHL5MW+78gIt5X0HYqsMUmugbrp5aWlh4/m5mZmQ1HDi/lb3fg1Ij4TRcL9g/Jbd4CLC8+MCJuB27P71dLAvgOsDNwmKSTgdXAGODHQPsmvRLrVUVFBVVVVTQ2Nq63r6qqioqKihJUZWZmZlYevOal/L0JWJrfF/6+RpMW8wN8DPhZH/oaC5xIWjtzdURMzutn3gbsAvx3QCq2DVZdXc2KFStobm6mubmZww477IX3vk2ymZmZDXceeSljkt4EPBARrXlTB/C4pBrSCMonJFUAt0bEDX3ocj5petkdRdu/C+wGfHFgKreNUV1d/UJIaWxs9F3HzMzMzDJFeA12OZM0NiJWdrF9RESs7eqYTaWhoSGWLl3ae0MzMzMzs40gaVlENBRv97SxMtdVcMnbN2twsdI499xzS12CmZmZWdlweDErY8uXr3cfBjMzM7Nhy+HFzMzMzMwGBYcXMzMzMzMbFBxezMrYwoULS12CmZmZWdlweDErY4sXLy51CWZmZmZlw+HFrIwtWLCg1CWYmZmZlQ2HFzMzMzMzGxQcXszMzMzMbFBweDErY6effnqpSzAzMzMrG6NKXYBtfpJGR8SqUtdhXWttbaWjowOAkSNH8sADDwBQUVFBdXV1CSszMzMzKy2Hl0FA0sHA2oi4XlIF8PWImFawfwbwI6AeeAp4GvhSREyVNA24OCKeLQgtJ0p6AqgE7omI327mS7JutLa2Ul9fT1tb23r7qqqqWLFihQOMmZmZDVsOL4PDcuCHwPXAO4B7i/YvBb4DzAS+DxwNPC/p3cCkiOi8ZdXVksYAE4A1wDjgSUnPAI9ExPs3+ZVYjzo6Omhra6OpqYmampoXtre0tNDY2EhHR4fDi5mZmQ1bDi9lTlINKZzcLen6gu0nRkS9pK2Ah4H3kUZSLgTeDOxECie/KOjuHOAAYFfSCM22+diHga02+cVYn9XU1FBbW1vqMszMzMzKisNL+XseWBIRRxVulHRjfvtS4ALgZ8D9pBDzU+CivH8OcEN+/xrgcFJQ6cg/dwaeyH3uHBEPFp1nOjAdYOLEiQN4WWZmZmZm/ePwUv5GALsUjrpkawAiYgXwDknjgVcBrwe2KWi3FkDSbkAd8G9S4HmQFGYAfg/sB6y30CIi5gPzARoaGmJgLsnMzMzMrP8cXspfB/BGYAxwGPCPiFgmaZ/OBfiStgP+lzTicmlEfLbzYElL8ts2YA9gB2BR3ta5dmYv4NUR0bHJr8b6pKWlpcfPZmZmZsORw0v5WwKcC+xNGgH5CbBP/vwJSR8khZrOIHKUpIbiTiLiMeAgSVdExImSTgRWRcT3JV0TEfdshmuxXlRUVFBVVUVjY+N6+6qqqqioqChBVWZmZmblwQ+pLGOSXkWa5vUk6VbJ/wAOk7Q3cB/wOGnq1zTgCkCkkZcDI+JA4OAuuv21pEbgQ8BfJP2AdEMAKwPV1dWsWLGC5uZmmpubOeyww15479skm5mZ2XDnkZfythr4CtAKHC3p9Xl7HfCxiDhV0kuB/0bEI5Lqio7/NnBb5wdJ84B9gTuAD0bEA5KeA06TdF5E+HHuZaC6uvqFkOK7jpmZmZmtowivwR6qJI2MiDUFnzsfUrlBGhoaYulSD9KYmZmZ2aYlaVlErLcUwtPGhrDC4JI/b3BwsdJYtGhR743MzMzMhgmHF7My5vBiZmZmto7Di5mZmZmZDQoOL2ZmZmZmNig4vJiVsblz55a6BDMzM7Oy4fBiZmZmZmaDgsOLWRmbOXNmqUswMzMzKxsOL2ZmZmZmNig4vJiZmZmZ2aDg8GJWxo444ohSl2BmZmZWNhxezMqYw4uZmZnZOqNKXYCVB0l7R8Stpa5juGptbaWjo2O97WeddRY/+MEPSlCRmZmZWfnxyMsQIWknSZfl99tKqpK0QNJrCtp8R9ItkpZIelTShIIu/ECRTaS9vZ3Zs2fT3t7e5f7W1lbq6+upq6tb73XppZfS2tra7z7NzMzMhiKHl6FjJYCkHYGpwHuB54DnC9qsBo6KiMnAtcAaSSdIGtV5vA289vZ25syZ023Q6OjooK2tjaamJpqbm194NTU18eyzz3Y5ItNbn2ZmZmZDkaeNDQGSJgOvBEYDPwEeBuqAnYFXSVoLnAgIqJBUCYzJnw8HFmzumoejlpaWHrfX1NRQW1vbp+O668vMzMxsKHN4GRomAkEaPTkH+B7wWuBbwIURcS+ApJuBTwBrgI7cPiIiJHXZsaTpwHSAiRMnbtKLGOoaGxs363FmZmZmQ43Dy9DwHPAIQETcKukthTsljST9ru8kjcaMAS4ljbz0KCLmA/MBGhoaYmDLHl6ampqoqalZb3tLS0uPAaWr43o7xszMzGwocngZAiLiMknbFWw6kjQdbDfgVcAq4ATgg8CbSEHnbOAXm7fS4a27aWGdiqeC9TadzMzMzGy4cXgZgiLiu8B3JV1IwbQx4HOSfg58Ftg/bxstaWwp6hwuKisrmTVrFpWVlV3ur6iooKqqqsuRlPHjx1NRUdHvPs3MzMyGIoeXoUOAlBavjIyI1S/sSNtGAEeQ7j72PLAncHtEvC238Z3nNpHKykpmz57d7f7q6mpWrFjR5V3FTjvtNKqrq/vdp5mZmdlQ5PAydIzNrx2Bn0halbefTwo2NwIHAO8B9gDGA4sBJN0IXLeZ67UC1dXVXYaUBQt8IzgzMzOzTg4vQ0REtJDWuQC8tas2ks6JiABuza9OUyJizaat0DZEc3MzVVVVpS7DzMzMrCx4qtAwkoNLV9sdXMrUeeedV+oSzMzMzMqGw4uZmZmZmQ0KDi9mZmZmZjYoOLyYlbFp06aVugQzMzOzsuHwYlbGpkyZUuoSzMzMzMqGw4tZGZs6dWqpSzAzMzMrGw4vZmZmZmY2KDi8mJmZmZnZoODwYlbGJk2aVOoSzMzMzMqGw4tZGZsxY0apSzAzMzMrG6NKXYDZcNLa2kpHR8d62ysqKqiurl5v+7nnnusAY2ZmZpZ55GUTkjRG0oQuto+V1K/gKGmkpF5/X5Iq+tpff85vPWtvb2f27Nm0t7d326a1tZX6+nrq6urWe9XX19Pa2rreMcuXL+/XOczMzMyGMoeXASZpB0mTJU0GjgH+X+dnSQfncHEscKOkuyTdL2mJpD9J+l1BP7tL+q2kP0i6B7gaOFTSVZK+JukwSWO7KOFySfXd1DZW0pGSlgO/yX1cJ+laSXcN9HcxnLS3tzNnzpweg0VHRwdtbW00NTXR3Nz8wqupqYm2trYuR2T6ew4zMzOzoczTxgbe24FJwBKgAxgLbJf3jQVGRsR8YL6kqcCEiPiGpIOAgzo7iYh7JLUCo4GVwF+A3YAvAg8A5wDPSbobWAQ8A9QAAhZI2jUfswVwaEQ8AVwK7AGsBpYD1wDvBG7O7WwjtbS09LqvpqaG2traPh379NNP88ADD/Tat5mZmdlw4PAy8FYCtwLTWRcojgG2Ab4eEW0FbXcA/pPfTwKWFvV1B/A48BJSuFgGnAmsAS6LiN/mdvtKqiWNzpwJtJPCzTci4ioASQcCtwN3AVsDt5FC1TLge8AhXV2MpOn5Wpg4cWI/vobhqbGxccCPveKKKza4TzMzM7OhxOFl4K0FXgtcnt+/HLgW2Ac4XNKjEXFbbrsn8N38fhJwpKTjI+LQvO29wPOkEZsngYeACtJ0v1flKWjPACcAJwH3AVvl17eBoyV9khRobgB2ASYDlwGfAo4EHgPqgLMl3RoRTxVeTOcoEUBDQ0Ns9LczxDU1NVFTU9PlvpaWlh7DTVfH3nzzzey77759Ot7MzMxsqHN4GXhjgX8B9wPPAU8D9wK1wCrgvwB58f0+5FGNiDg6b78u/3wtKQDdRgpAlcArgd8A/wb+DIwHts/7PwJcAhyV6xhBGlG5F3goItbktS7jgQOBLwMTgIOBz5Cmnm0FvCi8WP90NyWsUPH0r56mk5155plMnTp1IEs0MzMzG7QcXgbeS4E/AmeTRk32Bi4A2oC5EXF/bjcVuD4iVnXVSUTcIekwYAZQDYwjTUkbA7QAHwL+JyLuAWZI2jNvvyl3MRroyPvJdz27EFhBGvHZLbfbAtgWeA1wCvDoAHwHw05lZSWzZs2isrKy2zYVFRVUVVV1OXpSVVVFRUXPN4rryznMzMzMhjKHl4G3J3AxKUR8E/g1aRTmSdKIDJImAjOBtxQemG9fvDq/nwDcA+wIvAK4EwjSSMuOwHVApaRK0sJ78vaDSdPVRgJvkXQWcHIOQ3sD7yGtiem8B+8EYEREzBrA72DYqaysZPbs2T22qa6uZsWKFf16zkt/z2FmZmY2lDm8DCBJVaQF+q8GqkhrSh4lhZgvAftL2pa0zuVTEfF4wbHbkNa9dG4bATxLunvYeOANBad6DjgfGBsR7cA+ORB9hXQb5jXA54BxEfH5zoPy1DGA/YFdSVPcVgLNA/UdWM+qq6t7DSmFTj/99E1YjZmZmdng4ue8DKxXAfOAh4HPAu8G/hwRNwJvI00duxeYFhHXFB27G2kR/YUAEfGvfKcwdXGeMRHxdES0AkgaB9xNuk3zX0l3LTsYeJekT3dxnseBs0h3HhsPvEJS3/+its2mrq6u1CWYmZmZlQ1F+AZSQ4GknYDHC9fQSBoFjI+IJwu2TQEejIj78uetSetvfluwHqdLDQ0NsXRp8d2cbVOaOnUqCxcuLHUZZmZmZpuVpGUR0VC83dPGhoiIeKSLbatJa20Kty0u+vwk627XbGZmZmZWtjxtzMzMzMzMBgWHF7MyNmXKlFKXYGZmZlY2HF7Myti0adNKXYKZmZlZ2XB4MStjM2fOLHUJZmZmZmXD4cWsjDU3+xE8ZmZmZp0cXszMzMzMbFBweDErY5WVlaUuwczMzKxsOLyYlbF58+aVugQzMzOzsuHwYlbGFi1aVOoSzMzMzMrGqFIXYDYUtba20tHRsd72iooKqqur+9zPokWLOOKIIwayNDMzM7NByyMvQ4ykij62G7mpaxlq2tvbmT17Nu3t7T22a21tpb6+nrq6uvVe9fX1tLa2Dsh5zMzMzIYbh5cSkVQt6cP5/RmSTuim3UJJt0haUvBqkzS+m64vl1TfTV9jJR0paTnwG0mHSbpO0rWS7hqYKxu62tvbmTNnTq+hoqOjg7a2Npqammhubn7h1dTURFtbW5cjMhtyHjMzM7PhxtPGSufNwMHAT4DngVXdtFsNHBURD3RukHR9Z3tJOwCLgGeAGkDAAkm7An8BtgAOjYgngEuBPXKfy4FrgHcCN+d21gctLS192l9TU0Ntbe0GHw8wd+7c/hdoZmZmNkQ5vJTO+4E9cxB5GbBa0jHAaOD7EfGT3G5NN8evAYiIfwH7SqoFrgbOBNqBc4BvRMRVAJIOBG4H7gK2Bm4DtgOWAd8DDunqJJKmA9MBJk6cuOFXO4Q0NjaW9HgzMzOz4crhpQRy0Ng9IvbKn08F2iPiR100D+BSSc8VbNsrIiIfOwo4ATgJuA/YKr++DRwt6ZOkQHMDsAswGbgM+BRwJPAYUAecLenWiHjqRSePmA/MB2hoaIiNvPQhoampiZqamm73t7S09BhQ+nP8zJkzWbhw4YYXa2ZmZjaEOLyURjVwmqQxEfF84Q5JYyNiZcGmtXQ9baxTLfBy4CPAJcBRefsI0ojKvcBDEbEmr3UZDxwIfBmYQJq69hnS1LOtgBeFF1tfd9PBihVPD+ttOpmZmZmZ9czhpQQi4nZIIUTSamBn0rSxo4E21gUQSGtY1iNJkawAZkjaE2gBbspNRgMdEXFPbj8BuBBYAewJ7JbbbQFsC7wGOAV4dMAudIiprKxk1qxZvT71vqKigqqqqi5HX6qqqqio6PmGcH09j5mZmdlwozz7yEqop2ljkn4AnNXFyMvb8mhKJWnhPcCOpKlja4GRpHUx44GTI+KOfHvk9wAHkRbsQxp9GRERs3qrs6GhIZYuXbohlzjs+DkvZmZmZhtO0rKIaCje7pGXEpA0AiAi1nazf2TaHWtJv6P11rzk7Wsioh3YR9JE4CvAsaTQ8jlgXER8vvOgHHYA9gd2BcYCK4HmAb1Ao7q6ul8hpTsOLmZmZmbrOLyUxnuAT+QpYy/IdxuDNOVrNmmR/SjWX/NyMAV3IZM0DribtLj/r8CT+VUpqTUivl5wmt2Ax4Evkqan7QW8QlJ1RPT89ETb7E466STmzZtX6jLMzMzMyoLDSwlExCLSAvm+tJ3axbbfFH1+TtLuwOMR8cLzYvKdyIofZnkzcElE3AcslrQ1MBWoBBxeyowfVGlmZma2jsPLEBERj3SxbTVpBKZw2+Kiz08C39201ZmZmZmZbbwRpS7AzLpXV1dX6hLMzMzMyobDi1kZmzt3bqlLMDMzMysbDi9mZWzBggWlLsHMzMysbDi8mJWxxYsX997IzMzMbJhweDEzMzMzs0HB4cXMzMzMzAYFhxezMnbRRReVugQzMzOzsuHwYlbGmpubS12CmZmZWdlweDErY+edd16pSzAzMzMrG6NKXcBwJ2ks8HxExAD1VxERHX1oNzIi1gzEOW19ra2tdHSs/2uoqKigurq6BBWZmZmZDX4eeRlgkmok/Sy/r5e0m6Sb8889JG1VdMgXgPf10udPJe0iqa6z7x5cLqm+m37GSjpS0nLgN5IOk3SdpGsl3dXXaxyu2tvbmT17Nu3t7T22a21tpb6+nrq6uvVe9fX1tLa2Dsh5zMzMzIYbj7wMvOeANTmk7AOMB7YE9s8//x/wVEH7lcCTxZ1Ieh0wP7fdFXglEECdpCXAOOBLwDJgEfAMUAMIWCBpV+AvwBbAoRHxBHApsAewGlgOXAO8E7g5t7MetLe3M2fOHI477jgqKyu7bdfR0UFbWxtNTU3U1NS8sL2lpYXGxkY6Ojp6HH0pPM+0adMG8hLMzMzMBjWHlwEkqRY4HagCvg78C5hMChXHAIqIC4oOq6CLEbCIuB1oyP3OA74GrAJOi4hTiprvm899NXAm0A6cA3wjIq7KfRwI3A7cBWwN3AZsRwo/3wMO2dDrHm5aWlr6tL+mpoba2toNPh5gypQp/S/QzMzMbIhyeBlYrwJagK2AC0jB5WfA7sBfgRGS3hIRNxYc81rgUVLweBFJlwA75I/fL9i+BLgzIk6RNAo4ATgJuC+feyvg28DRkj5JCjQ3ALvkmi4DPgUcCTwG1AFnS7o1IgpHhZA0HZgOMHHixP5/I0NQY2PjZjt+6tSpLFy4cKPOZ2ZmZjZUOLwMrIeAx4HdIuIvkt4LHEEaCdkbaImI+Z2NJW0PdAD7SfpWRKws6u840mjLjsCXI+KYfNwOwHdzm1rg5cBHgEuAo/L2EaQRlXuBhyJiTV7rMh44EPgyMAE4GPgMaerZVrx4Shu53vkADQ0NA3JTgcGueDpYsc7pYZvqeDMzM7PhyuFlAEXEXZK2K9j0HLAY+DspNLy+6JDPAT8EKoFZpBGSwv5WAkgqPtV44IncZgUwQ9KepFGfm3Kb0UBHRNyT+5gAXAisAPYEdsvttgC2BV4DnEIaBbIedDcdrFjx9LDeppOZmZmZWc8cXjatAKYAryN91w907pD0AWCXiPiUUjpZLOmDEXFxTx1KeinwLnLIkFRJWngPaYTmYGAtMBJ4i6SzgJMj4g5JewPvIY0ELc/HTABGRMSsjb7aIa6yspJZs2b1uFgf0u2Qq6qquhw9qaqqoqKios/nmTRp0saUbGZmZjakaIAeL2KZpJcA50bEcZJmALdFxBJJW5PWoXwM+DzwNuCAiGjLx21HGqX5I/C5iGgt6LOGPG0sh5fPAudFREtBm4nAV4BjgTWkUZ1xEfH5ovreB8wB/g2MJd3trDkiju/t2hoaGmLp0qUb9L0MN37Oi5mZmdmGk7QsIhqKt3vkZeCNJU3Z6nzf6dOkKVsihYvGiHjhFskR8R9J+5NCR/HvZXRnnxHxKHBq4U5J44C7SSM9fyXdevlJoFJSa0R8vaD5bqR1OV8krY/ZC3iFpOrCwGQbp7q6ekBCyrnnnsuMGTMGoCIzMzOzwc/hZYDl0ZAP5o9fJk3hIiLmFDQ7s/i43KaDFF6KtzeT7gzW3Tmfk7Q78HhErOrcnu9ENr6o+c3AJRFxH2mq2tbAVNK6G4eXMrN8+fLeG5mZmZkNEw4vm1BErN6M53qkm/M/WbRtcdHnJ1l35zIzMzMzs7K13sMRzczMzMzMypHDi1kZ8wMqzczMzNZxeDErY4sXL+69kZmZmdkw4fBiVsYWLFhQ6hLMzMzMyobDi5mZmZmZDQoOL2ZmZmZmNig4vJiVsdNPP73UJZiZmZmVDYcXszJWV1dX6hLMzMzMyobDi1kZO/nkk0tdgpmZmVnZGFXqAsyGktbWVjo6OtbbXlFRQXV1dQkqMjMzMxs6HF4GAUmKiOhj24qIWP+v5/XbjYyINRtf3dDV3t7O+eefz6mnnkplZWWv7VtbW6mvr6etrW29fVVVVaxYsaLPAabz3M8//3x/yzYzMzMbsjxtbIBJapBUJalJ0hhJp0t6q6QDJR2Z29wk6XpJKyQty6/787amov5eDVwvaUT+PFfSm3so4XJJ9d3UNlbSkZKWA7+RdJik6yRdK+muAfoKhoz29nbmzJlDe3t7n9p3dHTQ1tZGU1MTzc3NL7yamppoa2vrckSmt3M3NDRsYPVmZmZmQ49HXgbeCcD5wK7ANcD1wCzgauCHuc2aiDhQ0ueBm/K2yRExW9KSws4i4i5JfwQmSmoH3gV8sXO/pB2ARcAzQA0gYIGkXYG/AFsAh0bEE8ClwB7AamB5ru+dwM25nXWhpaWlX+1qamqora3d4H4K2x511FF9PsbMzMxsqHN4GXgBjAT+AXwG2A+4MCIuk3SapKuBEZJ+AewGHJSPe4mkPfOxAEj6JrBX7nN/YDugCrhW0rMRcWhE/AvYV1ItKSCdCbQD5wDfiIircl8HArcDdwFbA7fl/pYB3wMO6epiJE0HpgNMnDhxo7+cwaixsbFk/Zx77rlcdNFFA3J+MzMzs8HO4WUASToYmAxMBP4OfBT4DnCOpA8DzwPfzm0EHAXsThqZ6VS4tuUM4PmIWFt0HgHj8vtRpNGek4D7gK3y69vA0ZI+SQo0NwC75HNfBnwKOBJ4DKgDzpZ0a0Q8VXiuiJgPzAdoaGjo07qboaapqYmamppe27W0tPQYUPraT2FfDz/8cJ/rNDMzMxvqHF4GUET8Btgth5jPAXeTpmk9C5wQEa2dbSW9DPga8Djwxrz5PxFxVEF/z3Vznsh9AtQCLwc+AlxCCkSQ1jN9D7gXeCgi1uS1LuOBA4EvAxOAg0kjRItIoedF4cW6nwbWneLpYb1NJzMzMzOzvnF42TTGAktJ08J+B5wFnAp8AV5YhH8xKXDcFxH3SjoCeFNhJ5JeAywE/lPU/zbA9Ii4LSJWADPylLMW1q2hGQ10RMQ9ua8JwIXACmDPXBuktS7bAq8BTgEeHYDrHxIqKyuZNWtWn+40Bul2yFVVVV2OvlRVVVFRUdHvc3vkxczMzGwd9fEOvNZHkvYHrgDGAB2k6WEPABXAzIi4XNJLge1J609+DDwMHAAcHhGP9/N8laSF9wA7kqaOrSWtnVlDGmk5OSLukDQSeA9pnc3yfMwEYEREFE5d61JDQ0MsXbq0P+UNO37Oi5mZmdnGk7QsIta77apHXgbebcDZpLUrtwNvIQWJf5LuPEZEPCqplTSl7C/AMcCHCoNLvjXy6IhY2dVJJI0B1kZEO7CPpInAV4BjSaHlc8C4iPh85zF56hikxf+7kkaIVgLNA3Xxw111dfWAhpRFixZxxBFHDFh/ZmZmZoOZn/MygCRtAVxHGm25EfgmaeH+V4FXAN/K7b4G3EKapvV3YFJE3FHU3U7AEkldvoA/AK/N/Y0jra95O/BX0pS1g4F3Sfp0Ub+7kdbZnEUa+RkPvEKShwXK0KJFi0pdgpmZmVnZ8MjLAIqIZyS9NY9wjAD2jYgn8+7T87QtIuK0PvT1MLBPH8/7nKTdgccjYlXn9nwnsvFFzW8GLomI+4DFkrYGpgKVQCtmZmZmZmXK4WWARcSa/HMt8GRX+zbReR/pYtvqLmpYXPT5SeC7m6ouMzMzM7OB4mljZmVs7ty5pS7BzMzMrGw4vJiZmZmZ2aDg8GJWxmbOnFnqEszMzMzKhsOLmZmZmZkNCg4vZmZmZmY2KDi8mJUxP6DSzMzMbB2HF7My5vBiZmZmto7Di1kZO+mkk0pdgpmZmVnZcHgxK2Pt7e2lLsHMzMysbIwqdQHWd5K2AZ6MiLU9tKmIiI4+9DUyItYMaIHDTGtrKx0d63/VFRUVVFdXl6AiMzMzs6HNIy8lIGmEpJEF70fln5dKGpu3jyp4jc6HfgFo7KX7yyXVd3PesZKOlLQc+I2kwyRdJ+laSXcN1PUNZu3t7cyePbvXEY/W1lbq6+upq6tb71VfX09ra+tGnwOgrq6un1dgZmZmNnQ5vJTGa4FrJD0OXAO8G3gz8HRErMxt3gf8BWgBfirpYWAf4FJJSyT9BUDSDpJulnS9pHuBnYAFkv6Vt92cR2wALgXOAsYAy/O5HwAuAeZt8qseBNrb25kzZ06vwaKjo4O2tjaamppobm5+4dXU1ERbW1uXIzL9PQfA3Llz+3kFZmZmZkOXp42VQETcDhws6cqIeCeApMuARySdRRpdOZ8Uao4CLgLOBT4J/CQiDpd0Ze7rX8C+kmqBq4EzgXbgHOAbEXFV7v9A4HbgLmBr4DZgO2AZ8D3gkE1+4YNIS0tLn/bX1NRQW1vbr+N767vQggULmDZtWp/bm5mZmQ1lDi8lkEdCLgf2kPQ74NvAfhGxU57yVRERv5K0Wz4kSOHiOqBW0hJg+9zXKOAE4CTgPmCr/Po2cLSkT5ICzQ3ALsBk4DLgU8CRwGNAHXC2pFsj4qmiWqcD0wEmTpw44N9FuWps7G123qY9vtPixYsdXszMzMwyh5cSiIgngAM6R14kHQncKGkvYBLwe0mvIgWM3Uih5GHg98CbSFO+VuXuaoGXAx8hTf86Km8fQRpRuRd4KCLW5LUu44EDgS8DE4CDgc8Ai0ih50XhJSLmA/MBGhoaYmC/ifLV1NRETU1Nt/tbWlp6DCg9Hd/bsWZmZmbWNYeXEpM0BrgV+CMpUGwNfCAinpV0HimMLCKNunwcuBi4GxgvqTIiVgAzJO1JWh9zU+56NNAREffk80wALgRWAHuSQhHAFsC2wGuAU4BHN+0VDw7dTQcrVjwFrLfpZGZmZma24RxeSkBSNTAFqAd+BCyIiMWSXgI8EBHP5qa7A8cAWwJfz8f8EPg08CfgU5K+QVp4D7AjaSRlLTASeEteQ3NyRNwhaW/gPaQ1McvzMROAERExa9Nd8eBRWVnJrFmzqKys7LFdRUUFVVVVXY6gVFVVUVFRsdHnALjooot6bWNmZmY2XChi2MwEKhuS3gw0AD8GnictzP8oaVrYG0kL668DPgfMBB4HfkUKLTuT7lZ2MVATEZfnPicCXwGOBdbkY8dFxOeLzv0+YA7wb2AssBJojojje6u7oaEhli5duuEXPsRsjue83H777bzuda8bkL7MzMzMBgtJyyKioXi7R15KICJ+TwoqSKoBDgCmR8Q/8gL804FHI+KQ3OYG4OsRsUTSm4Avkhben5f3jyNNJQvgr8CT+VUpqTUivl5w+t1IYeiLpClpewGvkFQdEd0/nMTWU11dvckfRnneeeexcOHCTXoOMzMzs8HC4aXEIqKFNOrS+Xk1ae1LoQMjYk3efwvwott+RcRzknYHHo+IzoX8nXciG1/U183AJRFxH7BY0tbAVKAScHgxMzMzs7Ll8DIIdAaXXto80sW21aQRmMJti4s+Pwl8d2NrNDMzMzPb1EaUugAz656f8WJmZma2jsOLWRmbMmVKqUswMzMzKxsOL2ZlbOrUqaUuwczMzKxsOLyYmZmZmdmg4PBiZmZmZmaDgsOLWRmbNGlSqUswMzMzKxsOL2ZlbMaMGaUuwczMzKxsOLyYlbFzzz231CWYmZmZlQ2HF7Mytnz58lKXYGZmZlY2HF7MzMzMzGxQGFXqAqxvJG0fEY/3oV1FRHT0od3IiFgzMNUNP62trXR0rP81V1RUUF1dXYKKzMzMzIY+j7yUgKQxksb1sH+UpNEFn8cCSyR9pg/dXy6pvpt+x0o6UtJy4DeSDpN0naRrJd3V7wsZYtrb25k9ezbt7e09tmttbaW+vp66urr1XvX19bS2tm70OTotXLiwH1dgZmZmNrQ5vJTGgcD1kpbk1yOS/tr5GVgMHARphASYD3wF2F3S+ws7krSDpJslXS/pXmAnYIGkf+VtN0vaJje/FDgLGAMsB64BHgAuAeZt6osud+3t7cyZM6fXYNHR0UFbWxtNTU00Nze/8GpqaqKtra3LEZn+nqPT4sWL+3EFZmZmZkObp42VQERcDVzd+VnS9cBpEXFnYTtJlcCPgd9FxI8lXQL8VNKhwFcj4t6I+Bewr6Ta3OeZQDtwDvCNiLgq93UgcDtwF7A1cBuwHbAM+B5wyCa74EGmpaWlT/tramqora3t1/G99V1swYIFTJkypV/HmJmZmQ1VDi8lJOnvQOc6loskAVRGxJ6SDgG+CdwJHC7pXbndGGApcK2kU4CrgBOAk4D7gK3y69vA0ZI+SQo0NwC7AJOBy4BPAUcCjwF1wNmSbo2Ip4pqnA5MB5g4ceJAfwVlqbGxsaTHm5mZmVnXHF5KqzUi9i/cIOlP+W0TMDki/tnVgZLOAIIUPF4OfIQ0/euo3GQEaUTlXuChiFiT17qMJ01b+zIwATgY+AywiBR6XhReImI+adoaDQ0NsTEXO1g0NTVRU1PT7f6WlpYeA0pPx/d2rJmZmZl1z+GltLaSdFPRtgCIiKclbSHpRmBVUZsnI+I9+f0KYIakPYEWoLO/0UBHRNwDIGkCcGFuvyewW263BbAt8BrgFODRgbq4waq76WDFiqeA9TadbEOcfvrpA9KPmZmZ2VDg8FJaT3Ux8nJL4UfgXxFxVHdt8rqYa/LHHUkjKWuBkcBbJJ0FnBwRd0jaG3gPaU1M59MPJwAjImLWQF3UYFVZWcmsWbOorKzssV1FRQVVVVVdjqBUVVVRUVGx0efoVFdX16d2ZmZmZsOBIobFTKCyJOmvQPGtqUZExN55/0tIC+zvLmozMSJ2KeprIumOZMcCa4DPAeMi4vNF7d4HzAH+DYwFVgLNEXF8b/U2NDTE0qVL+3h1Q9vmes7L1KlTfbtkMzMzG3YkLYuIhuLtHnkprYiINxVukPSXgo+jgSVdjLzcXvR5HCngBPBX4Mn8qpTUGhFfL2i+G+kmAV8krY/ZC3iFpOqI6P4BJfYi1dXVfhilmZmZ2Wbm8FJCEfGqnrblxfpHddHmdUWfn5O0O/B4RLywPkbSKNIC/UI3A5dExH3AYklbA1OBSsDhxczMzMzKlsPLEBERj3SxbTVpBKZw2+Kiz08C39201dmG8jNezMzMzNYZUeoCzKx706ZNK3UJZmZmZmXD4cWsjM2cObPUJZiZmZmVDYcXszLW3Nxc6hLMzMzMyobDi5mZmZmZDQoOL2ZlrK8PszQzMzMbDhxezMrYvHnzSl2CmZmZWdlweDErY4sWLSp1CWZmZmZlw+HFrIw5vJiZmZmt4/BiZmZmZmaDwqhSF2A22LS2ttLR0bHe9oqKCqqrq0tQkZmZmdnw4JGXEpC0vaQtNuC4UZK27KVNRR/7Gtnf8w9V7e3tzJ49m/b29l7btra2Ul9fT11d3Xqv+vp6WltbB/Scc+fO7VN/ZmZmZsOBw8smIOm7kl6V339P0i2Slki6Mzf5MPD2vP/Tkq6UdJWkayVdI+n1Rf0tz29fC3y5l9NfLqm+m7rGSjoy9/cbSYdJui6f964NvNxBr729nTlz5vQpvHR0dNDW1kZTUxPNzc0vvJqammhra+tyRGZjz2lmZmZmiaeNDSBJo4A1wCrgaUljgNXAURHxgKQrc9Ptgc6V2K8AjgO2Aj4eEafnvhqBc4DngFpJ1+c2O0raHRgNzATuy309A9QAAhZI2hX4C7AFcGhEPAFcCuyRa1oOXAO8E7g5txvWWlpa+tympqaG2traDeqjP+1mzpzJwoUL+9TWzMzMbKhzeBlYU4FjgV1IoyT/Ah4HtpC0FSBJJ5DCSqOkxUB01VFENAH7kw76akSckUdUpkTE/KLm+0qqBa4GzgTaScHnGxFxVe7jQOB24C5ga+A2YDtgGfA94JCu6pA0HZgOMHHixH59GYNNY2NjWfRhZmZmZl1zeBlAEfET4CeSLgTOy6Mt3wPeA/wX2CoividpMilkHJ8P3Qb4A3CvpD9GxD6dfUraA9gzr1E5EbhI0rbAlhHxUB7tOQE4iTQKs1V+fRs4WtIn87luIIWqycBlwKeAI4HHgDrgbEm3RsRTRdc0H5gP0NDQ0GXQGiqampqoqanpsU1LS0uPAaUvffSlHzMzMzNbn8PLAJO0DHgauEbSWcBY4NfAQ8C7JI0G1gI7A/eTRj9WAtdGxHGSflHQ1yuBa4EPRMQaSZWkUZPvAt/PfdYCLwc+AlwCHJUPH0EaUbkXeCgfvxwYDxxIWjszATgY+Axp6tlWwIvCy3DS3VSwrhRP++ptOtmGOuKIIwasLzMzM7PBzuFl4D0aEe+QdBxp7ctLSGHheWBL0nqXV5LCxgXA3sAY4BBJS4BxAJJeC3yNtDalPfc9mhRITo2IPwJExApghqQ9gRbgpoK2HRFxT+5vAnAhsALYE9gtt9sC2BZ4DXAK8OgAfheDQmVlJbNmzaKysrLXthUVFVRVVXU5alJVVUVFRZ9u9tbnczq8mJmZma2jiCE9E2izk7SUNHrxUmA28JmImJT3XRkR75RUA/wVmAu8LLd7DrgwIo7LbUeTQs1XgOuAY4A3k9a83FNwvkrSwnuAHUlTx9YCI0k3DxgPnBwRd+SpZ+8BDiKFIkijLyMiYlZv19bQ0BBLly7t93cy1GzO57ycdNJJzJs3b0D7NDMzMyt3kpZFREPxdo+8DLx/R8Shkt5HWsB/VcE+5Z/HAjOAJ0nTvtY1SGtYRkXEc8AqSZDWpcwGTiOFHCR9CLgzIv4M7CNpIinoHEsKLZ8DxkXE5zv7zlPHIN0IYFfSlLaVQPMAXfuwUF1dvdkeRulbKZuZmZmt4/Ay8D6dfy4BPk+6sxiSbiCtg9kNeB3wvogISdsBPyo4/grS2pWf5s/jgJURcY+k+4FFktqAHYADct/jgLtJdy77KykUPQlUSmqNiK8X9L8b6Q5oXyStj9kLeIWk6ojo2xMWzczMzMxKwOFlgHVO6YqI/0jaJyKezZ8P6Gwj6f2R5+tFxDeBb/bQ37SC92cDZ3fR5rn87JfHI2JVwXlGkaaNFboZuCQi7gMWS9qaNEJUCTi8lJm6urpSl2BmZmZWNrzmxfrMa17MzMzMbHPobs3LiFIUY2Z9s2DBglKXYGZmZlY2HF7MytjixYtLXYKZmZlZ2XB4MTMzMzOzQcHhxczMzMzMBgWHF7MydtFFF5W6BDMzM7Oy4fBiVsaam/38UDMzM7NODi9mZey8884rdQlmZmZmZcPhxczMzMzMBgWHFzMzMzMzGxRGlbqA4U7SKyLivn4eMyoiVnezryIiOvrQx8iIWNOf8w5Vra2tdHSs/5VVVFRQXV1dgorWmTZtWknPb2ZmZlZOPPKyCUg6RNKXCz6/XdI782t8wfZdgFskndRNP6+SdJKkz0v6mqSLJf0W+JWk0d2c/nJJ9d30N1bSkZKWA7+RdJik6yRdK+muDb/i8tLe3s7s2bNpb2/vtW1rayv19fXU1dWt96qvr6e1tXXAz9kfU6ZMGdD+zMzMzAYzh5cBJOkNkhYD3wDeKel3kj4K/C/wFDAbWJXbbgP8DDgM2EvSpyWNLOryv0AL8DtgNfD9iDgoIt4REZ397CDpZknXS7oX2AlYIOlfedvN+VwAlwJnAWOA5cA1wAPAJcC8TfGdlEJ7eztz5szpU5Do6Oigra2NpqYmmpubX3g1NTXR1tbW5YjMxp6zP6ZOnTqg/ZmZmZkNZp42NoAi4jZJRwKLgEOAtRHxnKT3ALcCD0bEakmvJAWXMyPiFkm3Al8EbpP0XeAXwMuB84BngZfmz8dJagH+DYwDZkTEbcC+kmqBq4EzgXbgHOAbEXEVgKQDgduBu4CtgduA7YBlwPdyvUNKS0tLn9vU1NRQW1u7QX30p52ZmZmZbTiHlwEkqRL4CbAN8Engg5L2z7v3B/4oaQ/SCMiNwCxJXyUFlDXAr3O7v0TETZJOBo4GXgH8CrgHmJaPXQT8Q9Io4ATgJOA+YKv8+jZwtKRPkgLNDcAuwGTgMuBTwJHAY0AdcLakWyPiqaJrmg5MB5g4ceJAfVWbRWNjY1n0YWZmZmYDw+FlYB0JXAV8gTSS8VJgZd53B3ByRJwnqSEingeQ9HngpohYUtiRpN2A/wN2A5qANwBPAk8DZ5MCy/uBJ0ijMh8hTf86KncxgjSici/wUESsyWtdxgMHAl8GJgAHA58hhaGtSNPbXhAR84H5AA0NDbExX87m1tTURE1NTY9tWlpaegwofemjL/1sqEmTJg14n2ZmZmaDlcPLwJoPbAnsHxFHSfpF546IeFzSrZLeDNwr6eekEZeXA++S1JaPnRIRz0fEvcCrJF0H/E/ReX4MTI6IzjAxQ9KepPUxN+Vto4GOiLgHQNIE4EJgBbAnKRQBbAFsC7wGOAV4dKC+jFLrbipYV4qnffU2nWxzmTFjRsnObWZmZlZuHF4GUESEJIApkpYArypq8gjwhoj4vaSLgb8BjaTA0QDc1zkiU2AsaapX8akCXpiqdk3eviNpJGUtMBJ4i6SzSCM+d0jaG3gPaU3M8nzMBGBERMzawMsuO5WVlcyaNYvKyspe21ZUVFBVVdXlqElVVRUVFRUDfs7+OPfccx1gzMzMzDKHl4EnYHEeeVkEdIaMw4HjgQ/ldSpNwF4Fxx0CHCVpdOedxLJtgOO6OAcAEdEO7CNpIvAV4FjS+pnPAeMi4vMFbdfkcLU/sCspGK0EmjfukstLZWUls2fP7lPb6upqVqxYsdHPeenPOftj+fLlvTcyMzMzGyYcXgbeGEjPegF2iIiV+ZksV0bE5XnffGBncrAhhYnVwMXAn4HCf2rviIjJhSeQ9Ieiz+OAu3N/fyWtjXkSqJTUGhFfL2i+G/A46e5mR5EC1CskVUdE3x5qMsRUV1eX/GGUZmZmZtY7h5cBFhH/JY2gjIqIa/Pm90fE6oI20/vR31u62LZf0efnJO0OPF44apNHeMYXHX4zcElE3AcslrQ1MBWoBIZleDEzMzOzwUHr1nyb9ayhoSGWLl1a6jLMzMzMbIiTtCwiGoq3jyhFMWbWN4sXLy51CWZmZmZlw+HFrIwtWLCg1CWYmZmZlQ2HFzMzMzMzGxQcXszMzMzMbFBweDErY6effnqpSzAzMzMrGw4vZmWsrq6u1CWYmZmZlQ2HF7MydvLJJ5e6BDMzM7Oy4fBiZmZmZmaDgsOLmZmZmZkNCqNKXYCkHYBHIyK62T86IlYN0Ll2Av4TESsHor+BImkUMDYint6IPsry2spBa2srHR0d622vqKigurq6BBX13ZQpU0pdgpmZmVnZKOnIi6TtgduAz3Sxb3R+e6KkD0v6pKSDeunvs5Km99Dky8AHio65TdIW3fR3jKT9e7yI1E6S7pFUK+no3toXHLc8v31trq2ntndKuj6//iTpnKImPV6bpBGSxkh6o6Rz8/tRks6XtEtfay6V9vZ2Zs+eTXt7e7+Oa21tpb6+nrq6uvVe9fX1tLa2bpY6NtS0adM2y3nMzMzMBoOShRdJ2wAXA+8Gdpd0hiQVNLla0o3AyaRwcwpwrqSbJf0897GzpBskXSXpSuBDwEmSrsyvGyS9PLfdEngTcGlRKU9HxDO5zag8CtLpGWCPgppHSBpZfC151Og5oAWYJKmih+tulHSTpOuB2vzzQuDdOZjcKGn/3O5+SX+X9Dbg4Yg4MCIOzN/J8wV99nptwA7Ap4D3AXsBnwTeCBwAbCXptZJe2l3dpdbe3s6cOXP6HRo6Ojpoa2ujqamJ5ubmF15NTU20tbV1OSKzKerYUDNnztws5zEzMzMbDEoybUxSPSm4zIiIZZKOB74E3C7pW8AvgXNIf1jvCjwFbAs8nF9bAUTEg7lNZ7+nAu0R8aMuTjsdeAQ4XNLJwOq8fS9JVwD/BzwNnCxpDbATMBa4X9J7O08BXJjDwnQKAgSwM3BlbnOtpLdGxLPFRUREE7B/rverEXFG/j6mRMT8gmt5A/CTfO3P522jgWXAR4DCaXa9XltEXCxpCjAO2A44DPgbcBNQARwN/Bb4RRffXdloaWnZoPY1NTXU1tYOWH+bS3Nz82Y9n5mZmVk52+zhRdIkYAFptOGrkp7Ku7YGzgaOB24BXgMcTgoqHfnnzsATuZ+dI+LB/If/LGAtsDvwvKTJwO0R8c3cdkfgg8ATEfFz4OcF9SyJiHcVlHh53v4/wLiIuKCbS3lhlCOPGP0COCkiHuvj97AHsGceyTkRuEjStsCWEfEQLw4nARARqyQ9WdRPf67t66RwBel3vzfwo/z+b8CSLuqcTgpHTJw4sS+Xtkk1NjaWdX9mZmZmtuls9vASEcslvRl4NiJ+WLhP0gjSqEs9UAf8G3gp8CApzAD8HtgPaMufRwNvA14CXJW3vR74U0HXk4AzgDPzeY4FqiLi/B5K3ZE0leuwgj6267yxgKSrgYnASODP+fV64OrevgNJrwSuBT4QEWskVZLC23eB7wMPdXPcVqSpbIX6c23Psm60aKuI+IKkdwIHRcQpXZ0zjwbNB2hoaOjypgqbU1NTEzU1NX1u39LS0mNAGej+BlplZeVmO5eZmZlZuSvV3ca+CdRLWgXsAvw9bx8B3AOcRVprsgOwKO+7N//cC3h1RHQARMQ9wPZ5fcwJwKOkYPCzzpNFxFUAks7Mm34LXAOc30ONBwFH5/6RtLTwjmgR8XZJx5FGZ74jaT/gw/QSXiS9FvgasBxoz5tHA98DTo2IPxY0Pw5YA3w0t6llXbAZ259rk3QA8BXSKM42wBN5Mf92pGljg0J30796Uzzdq7fpZOVi3rx5pS7BzMzMrGyUJLxExEcljSHdIev3wHnAbGBWwTqRgyRdEREnSjoRWBUR35d0TWegKDIXmAf8A7giIp7sok3n+f8pae/u9ueAMb4guGxFWg/TXfsdSAHs1ZImkL7XtoLF8oX+AryLFCReKWkO8GbSmpfC6xpJmtL1FDAmIt6W2/6ZNN3rf/tzbRFxg6QfAXfn898VEVdIuhjYVtIW3dRbFiorK5k1a1a/RyIqKiqoqqrqcrSkqqqKior+5bYNrWNDLVq0iCOOOGKznMvMzMys3JVqwf4bSSMjd5JGFBYCewL7Snp756gK8GtJjaS7iJ0m6QfA0m66bQdeB0wGPi5pXEQ8V3zqzjcFz0MZU1TbzqT1LCcUbN4VeLygzQTgGOAdwCrSWpyLSIHkO8ADpBGk+RTJz6xZlW+s9hgptJ1GulsZkj6Uv5d/k9b+/B5YI+kI4K2kEaELgHMknVEwGtTjteWpam8kLfh/GnhzHvlaC3wV+KGk47q6yUA5qKysZPbs2f0+rrq6mhUrVgzYc142tI4N5fBiZmZmtk6ppo09Qlqwf39+/yjwX1IY2BHokDQP2Be4A/hgRDwg6TlSiDkvIk7Pt0E+G3g1KSwcThqp+CzwNUkrgWMioimvpym8OxiSrgVWFHweSQofM/Jdwch3P3sX6fbEnZ4ihYUPRsS/Cra3SKoB3gt8oZfvYBywMiLukXQ/sEhSG2mq3AF54f/9uYazSdPoDsvhYrqkLwEvAx7qy7UB9wGnA9eTbpO8DPgVcFRE/DuHxNcChdPWhoTq6uqyfxilmZmZmfVO3TzYvuQkjc6jFL21Oxj4Y/E0sXwHsFcCzX3pp4f+R0XE6t5bdnnsTUWbVkbEWzdnDQOpoaEhli7tbuDLNoWpU6eycOHCUpdhZmZmtllJWhYRDcXbSzXy0qu+Bo6I+E0324N1NwLYmDo2ODRExP4be/6NrcEGt7lz55a6BDMzM7OyMaLUBZiZmZmZmfWFw4tZGZs5c2apSzAzMzMrGw4vZmZmZmY2KDi8mJmZmZnZoODwYlbG/IwXMzMzs3UcXszKmMOLmZmZ2ToOL2Zl7KSTTip1CWZmZmZlw+HFrIy1t7eXugQzMzOzsuHwYmZmZmZmg4LDi1kZq6urK3UJZmZmZmVjVH8PkLQD8GhERDf7R0fEqo2uLPW1E/CfiFg5EP1tRB2jgDXF1yxpfEQ8W7TtUODGiHhGUi2wdUTc1U2/I4Ddgb92933ahmttbaWjo2O97RUVFVRXV5egov6bO3duqUswMzMzKxv9GnmRtD1wG/CZLvaNzm9PlPRhSZ+UdFAv/X1W0vQemnwZ+EDRMbdJ2qKb/o6RtH+PF5HaSdI9kmolHd1be+As4EhJDZJ+nH9uA1xR1O8I4DxgR0m7APXAdEn1knaVVFXU77bAL/tw/sJzfEtSYxfbZ0p6Vw/HjZA0RtIbJZ2b34+SdH6utWy1t7cze/bsfq3/aG1tpb6+nrq6uvVe9fX1tLa2btLzD5QFCxZs9nOamZmZlas+h5f8x/rFwLuB3SWdIUkFTa6WdCNwMincnAKcK+lmST/Pfews6QZJV0m6EvgQcJKkK/PrBkkvz223BN4EXFpUytMR8UxuMyqPinR6BtijoOYRkkYWX0se5XgOaAEmSaro4bo/CIwBbgY+DKwEjgPeANwrac88QgRwKHAHsDdwGPBq4L78/jBgYu7z5ZKuBy4HtgZ+K+n6/HpDwbk/L+lGSb+X9LHOzUBbF6Wuyi8knVPwnf4k798B+BTwPmAv4JPAG4EDgK0kvVbSS7v7Hkqpvb2dOXPm9Cs8dHR00NbWRlNTE83NzS+8mpqaaGtr63JEZiDPP1AWL1682c9pZmZmVq76NG1MUj0puMyIiGWSjge+BNwu6Vuk0YNzSH8I7wo8RRpVeDi/tgKIiAdzm85+TwXaI+JHXZx2OvAIcLikk4HVeftekq4A/g94GjhZ0hpgJ2AscL+k93aeArgwB6HpwPMF/e8MXJnbXCvprcVTwLKfk0abzieFkhHAWuDZfP4fAjNyrZ8iBaLFwE9zm7XASOC/EXFe7nMc0BIRxxWeSNKXSWGG/H19ifQ9dwa/H5NC0wRJWwOnkr7rn5HCybOSXpvbHBIRqyU15b4ekTQln3s7Upj6G3ATUAEcDfwW+EUX30FZaGlp6XfbmpoaamtrB6QvMzMzMyutXsOLpEnAAuBC4KuSnsq7tgbOBo4HbgFeAxxOCiod+efOwBO5n50j4sEchGaR/qjfHXhe0mTg9oj4Zm67I/BB4ImI+DkpQHTWsyQiCqdHXZ63/w8wLiIu6OZSXhjBySNGvwBOiojHerr+iHhe0jeAuaSAsIYUGH5CCmrbR8QSSe8E/gGMB9pJU97WdJ6SFwentcDBefSlUD1wbcF3cDlpNAnSuphjJV1MCktnA89FRAswWdLpwN3ADcDkgj7XFLz/eq4F0u9+b+BH+f3fgCXF15+n9U0HmDhxYpff0ebS2LjebLmy6MvMzMzMNo9ew0tELJf0ZuDZiPhh4b68xuOXpD+664B/Ay8FHiSFGYDfA/uxbqrTaOBtwEuAq/K21wN/Kuh6EnAGcGY+z7FAVUSc30OpOwLvlnRYQR/bdS6El3Q1adrWSODP+fV64Oqerl/Sx4HXAQ+QpoytJY3y/BfYhXXf4U3APcD/5m2dQSdIgeHpoq5/083IS6dHSaNUVwDvKdg+AfgPUMm6YLgNKUi9Hdgyn7Mrz7IuRG0VEV/IoeugiDilqwMiYj4wH6ChoaGkNxVoamqipqamT21bWlp6DCgD2demdNFFF5XkvGZmZmblqK93G/smUC9pFekP9r/n7SNIf7CfRVprsgOwKO+7N//cC3h1RHQARMQ9wPZ5fcwJpD/SryVNfSK3uQpA0pl502+Ba0hTt7pzEHB07h9JSwvv4BURb5d0HGl05juS9iOtYekxvABLgf9HGk36JWna1Tmk0Y/3AQ/l/tsL1t88D0wr6mc5L17g3+PIS0SszWtpHgYagP0lnQNsk0eDtgE6JE0Evktaw/PViLg2j0J1Uv4+DgC+Qgo22wBPKN34YDvStLGy190UsJ4UT/nqbTpZuWlubqaqqvg+D2ZmZmbDU5/CS0R8VNIY0lSo35PuqDUbmFWwTuQgSVdExImSTgRWRcT3JV3TGSiKzAXmkaZaXRERT/Zw/n9K2ru7/Xmdx/iC4LIV6490FLbfgRTAXi1pAul7aOu8EUDRuW+RdEz++Eiu+TXArcAlpFGkYquBpyLinQXnvKqoTY8jL3lU60LSaNaJwJ6kEZdrcpPREbGGFJ4OzdPGOo0ArpMUpFEiIuIGST8iTS17F3BXRFyRp6FtK2mLrq6/HFRWVjJr1iwqKyv7fExFRQVVVVVdjphUVVVRUdH3vLYh5x8o5513HgsXLtzs5zUzMzMrR31dsP9G0sjInaRpXwtJf0zvK+ntnaMqwK+VbuP7IeA0ST8gjVx0pZ00HWsy8HFJ4yLiueJTd74peNbLmKLadiatZzmhYPOuwOMFbSYAxwDvIN2Ra2fgItJIxHdIU8LuIU+P6uorIN357HGgCngLafTiQWBfUpCBNCUN0ujGPpKWFPRRfDOAvoy83Eha/H8LcAjwBeAQSa8jra0ptAVpShukGxcckBfsL8vfwStJdxdbRgp2b84jaWuBrwI/lHRcNzctKKnKykpmz57dr2Oqq6tZsWLFgDznZUPOb2ZmZmYDr6/Txh4hjQLcn98/Slrz8Q7SWpMOSfNIf8jfAXwwIh6Q9BwpxJwXEafn2yCfTbqF8D2kBf5PAZ8FviZpJXBMRDTlkYfCRe5IuhZYUfB5JCl8zIiIprztW6SRhZMLDn2KFEA+GBH/KtjeIqkGeC8pGHRnLCmYnEu6m9i783cwCfiSpPfnGwuMI4Wr0cAfi0ZeflPQ32i6H3kZ2/k5Iubm7duQRl+Oiogn8sjW1wqOO4a0Lub7edP3WLdQ/4j88z7gdOB60m2SlwG/yn3+O4fO1wJ/7OF7GFSqq6sHzcMozczMzKx3GqgHu0saHRGr+tDuYNIf9k8WbRfwSqC5L/300P+oiFjde8suj72paNPKiHhrF+1GRMTa4u1DXUNDQyxd2t1Amm0KixcvZsqUKaUuw8zMzGyzkrQsIhqKt/d15KVXfQ0cEfGbbrYH624EsDF1bFBwycfu38d2wy64WGk4uJiZmZmtM6LUBZhZ96ZOnVrqEszMzMzKhsOLmZmZmZkNCg4vZmZmZmY2KDi8mJWxSZMmlboEMzMzs7Lh8GJWxmbMmFHqEszMzMzKhsOLWRk799xzS12CmZmZWdlweDErY8uXLy91CWZmZmZlw+HFzMzMzMwGBYcXMzMzMzMbFJQebG/WO0n/Bh4sdR3DzHbAf0pdhA0I/y6HBv8ehw7/LocG/x6HjuLf5c4R8ZLiRg4vZmVM0tKIaCh1Hbbx/LscGvx7HDr8uxwa/HscOvr6u/S0MTMzMzMzGxQcXszMzMzMbFBweDErb/NLXYANGP8uhwb/HocO/y6HBv8eh44+/S695sXMzMzMzAYFj7yYmZmZmdmg4PBiZmZmZmaDgsOLWRmSNE7SlZLulPRTSSp1TbbhJI2W9OtS12EbR9KPJd0i6QpJo0pdj20YSaMk/VzSHyT9oNT12MaR9GlJ15e6Dttwkt4gqUXSTfm1a0/tHV7MytMxQEtE7AVUAQeVuB7bQJLGA8vw73BQk7Q/MCoi3gRUAAeXuCTbcIcDd0bEfsAOkl5b2nJsQ0naGTi21HXYRqsCvh0R++fX33pq7PBiVp6mAL/N7xcDB5SwFtsIEfFsRLwGaCl1LbZRHgMuyO/9/87B7Vrg63n0rBLoKG05thEuAD5X6iJso1UBR0i6VdKi3mab+D/AZuVpW+CJ/L4DqC5hLWbDXkT8IyJulfQeYC3wm1LXZBsmIp6KiGeAPwCPRcT9pa7J+k/SVOBO4K+lrsU22grgfyNib2AH4C09NXZ4MStP/wG2ye+3yZ/NrIQkvQv4JHBYRKwudT22YSRtK2kssC9QJckj24PTO4G3ApcCr5f08RLXYxvuAeD6gvfb99TY4cWsPP2OdXPqpwA3lLAWs2FP0kuBGcA7I+LJUtdjG+U04P0RsQZ4Bhhf4npsA0TE1IjYHzgKWBYRF5a6JttgnwaOkjQC2BO4u6fGDi9m5eliYCdJfwZaSWHGzErnWNJ0huvy3XA+UuqCbINdBHxE0h+B/wLXlbges+HuQuB44E/ALyOix6mAiojNUpWZmZmZmdnG8MiLmZmZmZkNCg4vZmZmZmY2KDi8mJmZmZnZoODwYmZmZmZmg4LDi5mZGaBkt3603y3f2rO/59mjv8cMlA2t2cysXPg/YGZmZsnngVf0o/0r8jF9JulYenl6dA/HVkmq2pBjC/S7ZjOzcuLwYmZmQ4KkJRtxbB2wXURcJWm2pGPy9mPy59mS/ibpFkm/kVQREVcB2+Zj+3KOCuDgiPi2pNGSrpD0B0nz8v6XSPp3fo7MTZImS3q3pL9KehlwCNDtAzIlfUzSbZKul7RTwfYVne/7W7OZWblxeDEzM4MPAd/upc0XI+JNwC3A1Lzt2/nYvjgc+Gl+fzAwMiL2A0ZKmgxUAZdHxP75tQR4G/AZYF9gRESs7qrjHG4+CewPfB34bA919KdmM7Oy4vBiZmZDjqSxki7JIxsLJY2RNF7StZKWSvqppJkFh7w8Iu7tY/dbAKsB8jF9HcWYRHqCNICArQveTyKFl30l3SxpiaQtgbXAOGAycEMPfR8I3BARK4E/Ard117CfNZuZlRWHFzMzG4pOAP6aRzb+ARwP7AY8DOwD7BIRc3s4fmaehjazaNv9pD/8Ly7Yrj7WNB54Jr+/FnhQ0lXAlnnff4BzImJf4J/A+4HLSCMq/wDOlzR1vV6TCUArQES0RcRPeqmlrzWbmZUVhxczMxuK9iCNQJB/7gE8ArwOuBH4ZlH75/JIR6e5ETEZKAw4c4EzgZaIeBYgH/NsH2t6GKjN78cCn4mId5CCyuOk8PLLvP8BYPuI+D3wQeAhoBl4azd9dwBb5ZpeJukb3RXRz5rNzMqKw4uZmQ1FfwHelN+/KX8+BPhSROwbERcXtb8WeE8f+v0F8FZJ/7+9O1ilIIrjOP79lwfAE2BhQ1laKMkLYGMnK7yA17C1kqxYyIYSG0WUhZJrz1JeQEn6W8yR243LtdAdfT+rmTn/Of23v5k5Z3rL+Rxw/MOeDqnepkC169dBOZ4CToB5YLVcGwNum8bPgRcgv5j7ApiOiB6q9TGvbfropGdJ6iqGF0nSf7QBjETEBTAMbAHXwHpEnEbEbkSMNtXvA7MR0d9u0rJgfhNYLrUz5d5vZeY1MBQRg5nZABoRcQXsZeYdsA2MR8Ql8AAclX+yPAGPwAQfgaZ17htgh2ozgRVg7bO6TnuWpG4TmV89xJEk6f+IiCVgEXimCgRrZUev9/EBYPIH60Xe6xeBs8y876CHPmAhM1s/W/sTv+lZkrqJ4UWSJElSLfjZmCRJkqRaMLxIkiRJqgXDiyRJkqRaMLxIkiRJqgXDiyRJkqRaeAN0WQqqB73KtwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(12,7)) #画布\n",
    "ax.set_title('各个特征的留存相关系数') #图题\n",
    "cph.plot(ax=ax) #绘图"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
